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1
Tougas, Francine, France Veilleux, and Lise Dubé. "Privation relative et programmes d'action positive." Canadian Journal of Behavioural Science / Revue canadienne des sciences du comportement 19, no. 2 (1987): 167–76. http://dx.doi.org/10.1037/h0080014.
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Elie, Serge. "Fieldwork in Soqotra: The Formation of a Practitioner's Sensibility." Practicing Anthropology 34, no. 2 (March 29, 2012): 30–34. http://dx.doi.org/10.17730/praa.34.2.7279k63434142762.
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In this article, I undertake a sort of intellectual biography of my fieldwork among Soqotrans as a kind of "participant objectivation," hopefully without falling into the idiosyncratic self-exploration of the traditional confessional tale from the field. The aim is to describe my emancipation from the gamut of awkward sentiments that inform the "congenial orthodoxies" of ethnographic research and the subsequent formation of my practitioner's sensibility. The latter term is not to be associated with a guilt-driven empathy or humanitarian pity engendered by the perceived privations of the "other" relative to "us" that inform much of our ethnographic encounters and representations; rather, it refers to an intellectual and affective disposition toward fieldwork that is molded through a keen responsiveness to field interlocutors' practical interests.
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Lombardo, Nicholas E. "Privation, Teleology, and the Metaphysics of Evil." Theological Studies 84, no. 2 (May 26, 2023): 293–311. http://dx.doi.org/10.1177/00405639231169954.
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Drawing inspiration from Pseudo-Dionysius, Maximus the Confessor, and Thomas Aquinas, and in support of the definition of evil as the privation of being or goodness, this article proposes a complementary definition of evil. It argues that evil can be defined as the non-advancement of being, appetite, or natural inclination toward its proper perfection or completion. First, it explains what this definition entails, elaborates on its implications, and defends its plausibility. Second, it discusses typical objections to the privation account and shows how defining evil relative to appetite can help overcome them.
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Pringuey, D. "Privation de sommeil dans la dépression." Psychiatry and Psychobiology 3, no. 6 (1988): 419–26. http://dx.doi.org/10.1017/s0767399x00002303.
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RésuméBien que la maladie dépressive se manifeste par des troubles majeurs du sommeil, reposant sur une désorganisation architecturale assez spécifique, la privation totale du sommeil (DTS) d’une nuit apporte un allègement symptomatique notable le plus souvent immédiat, contemporain de la privation.A la suite des observations fortuites de Schulte, des séries systématiques et contrôlées ont défini le cadre et les limites de l’efficacité de la PTS et diverses investigations biologiques ont cherché à identifier des variables prédictives de son activité.La PTS apporte une amélioration dans environ 60% des cas, plus régulièrement dans les formes endogènes et lorsqu’il y a des variations diurnes de l’humeur. Cette amélioration est globale et elle apparaît le plus souvent aux heures de l’aube mais elle est transitoire, le bénéfice étant perdu au lendemain de la nuit de récupération d’où la nécessité de répéter la privation ou d’y associer une chimiothérapie antidépressive.Les investigations neurobiologiques situent la réponse clinique en rapport avec diverses modifications sur les paramétres catécholaminergiques et neuro-endocriniens ainsi que sur la structuration du sommeil.L’activation thymique se corrèle avec une facilité relative à maintenir l’éveil et se manifeste à un horaire particulier qui correspond à une époque circadienne critique. La PTS produirait une réorganisation des rapports entre la veille et le sommeil et parviendrait à rétablir les coïncidences de phases des rythmes biologiques. Ces données apportent une contribution nouvelle à la notion spécifique d’instabilité dépressive.
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Lagacé, Martine, and Françine Tougas. "Les répercussions de la privation relative personnelle sur l'estime de soi." Les cahiers internationaux de psychologie sociale Numéro 69, no. 1 (2006): 59. http://dx.doi.org/10.3917/cips.069.0059.
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Alain, Michel. "La Théorie de la Privation Relative Appliquée au Monde du Travail." Applied Psychology 38, no. 3 (July 1989): 251–63. http://dx.doi.org/10.1111/j.1464-0597.1989.tb01256.x.
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Edelen, Roger M., Alan J. Marcus, and Hassan Tehranian. "Relative Sentiment and Stock Returns." Financial Analysts Journal 66, no. 4 (July 2010): 20–32. http://dx.doi.org/10.2469/faj.v66.n4.2.
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Ghag, Kranti Vithal, and Ketan Shah. "ARTFSC Average Relative Term Frequency Sentiment Classification." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 6 (February 14, 2014): 3591–601. http://dx.doi.org/10.24297/ijct.v12i6.3141.
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Sentiment Classification refers to the computational techniques for classifying whether the sentiments of text are positive or negative. Statistical Techniques based on Term Presence and Term Frequency, using Support Vector Machine are popularly used for Sentiment Classification. This paper presents an approach for classifying a term as positive or negative based on its average frequency in positively tagged documents in comparison with negatively tagged documents. Our approach is based on term weighting techniques that are used for information retrieval and sentiment classification. It differs significantly from these traditional methods due to our model of logarithmic differential average term distribution for sentiment classification. Terms with nearly equal distribution in positively tagged documents and negatively tagged documents were classified as a Senti-stop-word and discarded. The proportional distribution of a term to be classified as Senti-stop-word was determined experimentally. Our model was evaluated by comparing it with state of art techniques for sentiment classification using the movie review dataset.
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Jiang, Cuiqing, Jianfei Wang, Qian Tang, and Xiaozhong Lyu. "Investigating the Effects of Dimension-Specific Sentiments on Product Sales: The Perspective of Sentiment Preferences." Journal of the Association for Information Systems 22, no. 2 (2021): 459–89. http://dx.doi.org/10.17705/1jais.00668.
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While the literature has reached a consensus on the awareness effect of online word-of-mouth (eWOM), this paper studies its persuasive effect—specifically, dimension-specific sentiment effects on product sales.We examine the sentiment information in eWOM along different product dimensions and reveal different persuasive effects on consumers’ purchase decisions based on consumers’ sentiment preference, which is defined as the relative importance that consumers place on various dimension-specific sentiments. We use an aspect-level sentiment analysis to derive dimension-specific sentiment and PVAR (panel vector auto-regression) models, and estimate their effects on product sales using a movie panel dataset. The findings show that three dimension-specific sentiments (star, genre, and plot) are positively related to movie sales.Regarding consumers’ sentiment preferences, we find a positive relationship to movie sales that is stronger for plot sentiment, relative to star sentiment for low-budget movies. For high-budget movies, we find a positive relationship to movie sales that is stronger for star sentiment, relative to plot or genre sentiment.
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Jagsi, Reshma, John A. E. Pottow, Kent A. Griffith, Cathy Bradley, Ann S. Hamilton, John Graff, Steven J. Katz, and Sarah T. Hawley. "Long-Term Financial Burden of Breast Cancer: Experiences of a Diverse Cohort of Survivors Identified Through Population-Based Registries." Journal of Clinical Oncology 32, no. 12 (April 20, 2014): 1269–76. http://dx.doi.org/10.1200/jco.2013.53.0956.
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Purpose To evaluate the financial experiences of a racially and ethnically diverse cohort of long-term breast cancer survivors (17% African American, 40% Latina) identified through population-based registries. Methods Longitudinal study of women diagnosed with nonmetastatic breast cancer in 2005 to 2007 and reported to the SEER registries of metropolitan Los Angeles and Detroit. We surveyed 3,133 women approximately 9 months after diagnosis and 4 years later. Multivariable models evaluated correlates of self-reported decline in financial status attributed to breast cancer and of experiencing at least one type of privation (economically motivated treatment nonadherence and broader hardships related to medical expenses). Results Among 1,502 patients responding to both surveys, median out-of-pocket expenses were ≤ $2,000; 17% of respondents reported spending > $5,000; 12% reported having medical debt 4 years postdiagnosis. Debt varied significantly by race: 9% of whites, 15% of blacks, 17% of English-speaking Latinas, and 10% of Spanish-speaking Latinas reported debt (P = .03). Overall, 25% of women experienced financial decline at least partly attributed to breast cancer; Spanish-speaking Latinas had significantly increased odds of this decline relative to whites (odds ratio [OR], 2.76; P = .006). At least one privation was experienced by 18% of the sample; blacks (OR, 2.6; P < .001) and English-speaking Latinas (OR, 2.2; P = .02) were significantly more likely to have experienced privation than whites. Conclusion Racial and ethnic minority patients appear most vulnerable to privations and financial decline attributable to breast cancer, even after adjustment for income, education, and employment. These findings should motivate efforts to control costs and ensure communication between patients and providers regarding financial distress, particularly for vulnerable subgroups.
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Wijayanti, Rini, and Andria Arisal. "Automatic Indonesian Sentiment Lexicon Curation with Sentiment Valence Tuning for Social Media Sentiment Analysis." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 1 (April 2021): 1–16. http://dx.doi.org/10.1145/3425632.
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A novel Indonesian sentiment lexicon (SentIL -- Sentiment Indonesian Lexicon) is created with an automatic pipeline; from creating sentiment seed words, adding new words with slang words, emoticons, and from the given dictionary and sentiment corpus, until tuning sentiment value with tagged sentiment corpus. It begins by taking seed words from WordNet Bahasa that mapped with sentiment value from English SentiWordNet . The seed words are enriched by combining the dictionary-based method with words’ synonyms and antonyms, and corpus-based methods with word embedding for word similarity that trained in positive and negative sentiment corpus from online marketplaces review and Twitter data. The valence score of each lexicon is recalculated based on its relative occurrence in the corpus. We also add some famous slang words and emoticons to enrich the lexicon. Our experiment shows that the proposed method can provide an increase of 3.5 times lexicon number as well as improve the accuracy of 80.9% for online review and 95.7% for Twitter data, and they are better than other published and available Indonesian sentiment lexicons.
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Zhang, Ping, Lin Zhang, Zhenghui Meng, and Tewei Wang. "Dynamic Spillover Effects of Investor Sentiment and Return between China and the United States." Discrete Dynamics in Nature and Society 2021 (August 4, 2021): 1–19. http://dx.doi.org/10.1155/2021/6622261.
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As the two largest economies in the world, the investor sentiment and stock return of China and the United States are the focus of global attention. In this paper, we study the dynamic spillover effects of investor sentiment and return between China and the United States. First, we use the relative price differences of 9 dual-listed companies in China and the United States simultaneously to verify whether investor sentiment affects stock returns. We find a significant positive correlation between the relative price difference of dual-listed companies and the difference of investor sentiment, indicating that the investor sentiment index indeed affects stock prices. Next, we construct the TVP-VAR model to study the dynamic spillover effects of investor sentiment and the return between China and the United States. Through the time-varying impulse response, we find investor sentiment has a significant dynamic impact on returns. Therefore, investment sentiment contagion and stock market linkage between China and the United States are obvious. In addition, we conduct various robust tests, and all results are consistent.
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Beer, Francisca, and Mohamed Zouaoui. "Measuring Stock Market Investor Sentiment." Journal of Applied Business Research (JABR) 29, no. 1 (December 27, 2012): 51. http://dx.doi.org/10.19030/jabr.v29i1.7555.
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Recently, investor sentiment measures have become one of the more widely examined areas in behavioral finance. A number of measures have been developed in the literature without having been fully validated, and therefore leaving in question which measure should be used for empirical exploration. The purpose of this study is to examine the relative performance of a number of popular measures in predicting stock returns and to test the relative efficacy of a hybrid approach. Using a panel of investor sentiment measures, we develop a new measure of sentiment which combines direct and indirect sentiment measures. Our results show that our composite sentiment index affects the returns of stocks hard to value and difficult to arbitrage consistent with the predictions of noise traders models. Finally, we find that our composite index has a better predictive ability than the alternative sentiment measures largely used in the literature.
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Huang, Jie, Yunpeng Cui, and Shuo Wang. "Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis." Applied Sciences 13, no. 1 (January 1, 2023): 603. http://dx.doi.org/10.3390/app13010603.
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Aspect-based sentiment analysis is a fine-grained sentiment analysis task that consists of two types of subtasks: aspect term extraction and aspect sentiment classification. In the aspect term extraction task, current methods suffer from the lack of fine-grained information in aspect term extraction and difficulty in identifying aspect term boundaries. In the aspect sentiment classification task, the current aspect sentiment classifier cannot adapt itself to the text and determine the local context. To address these two challenges, this work proposes an adaptive semantic relative distance approach based on dependent syntactic analysis, which uses adaptive semantic relative distance to determine the appropriate local context for each text and increase the accuracy of sentiment analysis. Meanwhile, the study also predicts the current word labels by combining local information features extracted by local convolutional neural networks and global information features to precisely locate the word labels. In two subtasks, our proposed model improves accuracy and F1 scores on the SemEval-2014 Task 4 Restaurant and Laptop datasets compared to the state-to-the-art approaches, especially in the aspect sentiment classification subtask.
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Srivastava, Akansha, and Ravindra Gupta. "Sentiment Analysis Techniques: A Review." Volume 5 - 2020, Issue 9 - September 5, no. 9 (October 3, 2020): 913–17. http://dx.doi.org/10.38124/ijisrt20sep653.
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Sentiments are the attitude, opinions, thoughts, beliefs or feelings of the writer towards something, such as people, artifacts, company or location. Sentiment analysis intends to conclude the judgment of a presenter or an author apropos to some subject matter or on the whole relative polarity of the manuscript. The outlook could be the perception or assessment, emotional condition, or the projected poignant message of the person behind
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Berkman, John, and Robyn Boeré. "St. Thomas Aquinas on Impairment, Natural Goods, and Human Flourishing." National Catholic Bioethics Quarterly 20, no. 2 (2020): 311–28. http://dx.doi.org/10.5840/ncbq202020229.
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This essay examines St. Thomas Aquinas’s views on different types of impairment. Aquinas situates physical and moral impairments in a teleological account of the human species, and these impairments are made relative in light of our ultimate flourishing in God. For Aquinas, moral and spiritual impairments are of primary significance. Drawing on Philippa Foot’s account of natural goods, we describe what constitutes an impairment for Aquinas. In the Thomistic sense, an impairment is a lack or privation in relation to that which is appropriate to the human being, known by our nature and ultimate perfection. For Aquinas, perfection lies in the transformation necessary for union with God.
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Sauper, C., and R. Barzilay. "Automatic Aggregation by Joint Modeling of Aspects and Values." Journal of Artificial Intelligence Research 46 (January 31, 2013): 89–127. http://dx.doi.org/10.1613/jair.3647.
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We present a model for aggregation of product review snippets by joint aspect identification and sentiment analysis. Our model simultaneously identifies an underlying set of ratable aspects presented in the reviews of a product (e.g., sushi and miso for a Japanese restaurant) and determines the corresponding sentiment of each aspect. This approach directly enables discovery of highly-rated or inconsistent aspects of a product. Our generative model admits an efficient variational mean-field inference algorithm. It is also easily extensible, and we describe several modifications and their effects on model structure and inference. We test our model on two tasks, joint aspect identification and sentiment analysis on a set of Yelp reviews and aspect identification alone on a set of medical summaries. We evaluate the performance of the model on aspect identification, sentiment analysis, and per-word labeling accuracy. We demonstrate that our model outperforms applicable baselines by a considerable margin, yielding up to 32% relative error reduction on aspect identification and up to 20% relative error reduction on sentiment analysis.
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Zargari, Hamed, Morteza Zahedi, and Marziea Rahimi. "GINS: A Global intensifier-based N-Gram sentiment dictionary." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 11763–76. http://dx.doi.org/10.3233/jifs-202879.
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Words are one of the most essential elements of expressing sentiments in context although they are not the only ones. Also, syntactic relationships between words, morphology, punctuation, and linguistic phenomena are influential. Merely considering the concept of words as isolated phenomena causes a lot of mistakes in sentiment analysis systems. So far, a large amount of research has been conducted on generating sentiment dictionaries containing only sentiment words. A number of these dictionaries have addressed the role of combinations of sentiment words, negators, and intensifiers, while almost none of them considered the heterogeneous effect of the occurrence of multiple linguistic phenomena in sentiment compounds. Regarding the weaknesses of the existing sentiment dictionaries, in addressing the heterogeneous effect of the occurrence of multiple intensifiers, this research presents a sentiment dictionary based on the analysis of sentiment compounds including sentiment words, negators, and intensifiers by considering the multiple intensifiers relative to the sentiment word and assigning a location-based coefficient to the intensifier, which increases the covered sentiment phrase in the dictionary, and enhanced efficiency of proposed dictionary-based sentiment analysis methods up to 7% compared to the latest methods.
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Hipson, Will E. "Using sentiment analysis to detect affect in children’s and adolescents’ poetry." International Journal of Behavioral Development 43, no. 4 (February 20, 2019): 375–82. http://dx.doi.org/10.1177/0165025419830248.
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Sentiment analysis is a computational method that automatically analyzes the valence of massive quantities of text. Basic sentiment analysis involves extracting and counting emotionally-laden keywords from passages of text (e.g., hate, love, happy, sad). This study describes using sentiment analysis to explore changes in emotion expression in a developmental context. A sample of n = 8,688 poems published online by children and adolescents from Grade 4 to Grade 12 was analyzed. Sentiment analysis coded words as positive or negative and these were averaged within each poem to obtain its relative percentage of positive and negative sentiment. Polynomial regressions explored linear and nonlinear trends in sentiment scores by grade. Among the results, negative sentiment demonstrated an upward curvilinear trend, increasing sharply from Grade 6 to Grade 11 and then decreasing afterward. Positive sentiment demonstrated a sinusoidal pattern throughout development. Overall, these findings are consistent with previous research on the progressions of emotion expression in childhood and adolescence. Despite some limitations, sentiment analysis presents an opportunity for researchers in developmental psychology to explore basic questions in emotional development using large quantities of data.
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Laplante, Joelle, and Francine Tougas. ""La privation relative et le niveau d'identification comme déclencheurs du désengagement psychologique : une étude exploratoire auprès d'éducatrices?" Les cahiers internationaux de psychologie sociale Numéro 89-90, no. 1 (2011): 43. http://dx.doi.org/10.3917/cips.089.0043.
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Ghag, Kranti Vithal, and Ketan Shah. "Conceptual Sentiment Analysis Model." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (August 1, 2018): 2358. http://dx.doi.org/10.11591/ijece.v8i4.pp2358-2366.
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<span>Bag-of-words approach is popularly used for Sentiment analysis. It maps the terms in the reviews to term-document vectors and thus disrupts the syntactic structure of sentences in the reviews. Association among the terms or the semantic structure of sentences is also not preserved. This research work focuses on classifying the sentiments by considering the syntactic and semantic structure of the sentences in the review. To improve accuracy, sentiment classifiers based on relative frequency, average frequency and term frequency inverse document frequency were proposed. To handle terms with apostrophe, preprocessing techniques were extended. To focus on opinionated contents, subjectivity extraction was performed at phrase level. Experiments were performed on Pang & Lees, Kaggle’s and UCI’s dataset. Classifiers were also evaluated on the UCI’s Product and Restaurant dataset. Sentiment Classification accuracy improved from 67.9% for a comparable term weighing technique, DeltaTFIDF, up to 77.2% for proposed classifiers. Inception of the proposed concept based approach, subjectivity extraction and extensions to preprocessing techniques, improved the accuracy to 93.9%.</span>
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Ban Kirigin, Tajana, Sanda Bujačić Babić, and Benedikt Perak. "Lexical Sense Labeling and Sentiment Potential Analysis Using Corpus-Based Dependency Graph." Mathematics 9, no. 12 (June 21, 2021): 1449. http://dx.doi.org/10.3390/math9121449.
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This paper describes a graph method for labeling word senses and identifying lexical sentiment potential by integrating the corpus-based syntactic-semantic dependency graph layer, lexical semantic and sentiment dictionaries. The method, implemented as ConGraCNet application on different languages and corpora, projects a semantic function onto a particular syntactical dependency layer and constructs a seed lexeme graph with collocates of high conceptual similarity. The seed lexeme graph is clustered into subgraphs that reveal the polysemous semantic nature of a lexeme in a corpus. The construction of the WordNet hypernym graph provides a set of synset labels that generalize the senses for each lexical cluster. By integrating sentiment dictionaries, we introduce graph propagation methods for sentiment analysis. Original dictionary sentiment values are integrated into ConGraCNet lexical graph to compute sentiment values of node lexemes and lexical clusters, and identify the sentiment potential of lexemes with respect to a corpus. The method can be used to resolve sparseness of sentiment dictionaries and enrich the sentiment evaluation of lexical structures in sentiment dictionaries by revealing the relative sentiment potential of polysemous lexemes with respect to a specific corpus. The proposed approach has the potential to be used as a complementary method to other NLP resources and tasks, including word disambiguation, domain relatedness, sense structure, metaphoricity, as well as a cross- and intra-cultural discourse variations of prototypical conceptualization patterns and knowledge representations.
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Wang, John, Qiannong Gu, and Gang Wang. "Potential Power and Problems in Sentiment Mining of Social Media." International Journal of Strategic Decision Sciences 4, no. 2 (April 2013): 16–26. http://dx.doi.org/10.4018/jsds.2013040102.
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Sentiment mining research has experienced an explosive growth in awareness and demand as Web 2.0 technologies have paved the way for a surge of social media platforms that have significantly and rapidly increased the availability of user generated opinioned text. The power of opinions has long been known and is beginning to be tapped to a fuller potential through sentiment mining research. Social media sites have become a paradise for sentiment providing endless streams of opinioned text encompassing an infinite array of topics. With the potential to predict outcomes with a relative degree of accuracy, sentiment mining has become a hot topic not only to researchers, but to corporations as well. As the social media user base continues to expand and as researchers compete to fulfill the demand for sentiment analytic tools to sift through the endless stream of user generated content, the growth of sentiment mining of social media will continue well into the future with an emphasis on improved reliability, accuracy, and automation.
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Dorn, Daniel. "Does Sentiment Drive the Retail Demand for IPOs?" Journal of Financial and Quantitative Analysis 44, no. 1 (February 2009): 85–108. http://dx.doi.org/10.1017/s0022109009090024.
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AbstractIndividual and institutional investors can trade German initial public equity offerings on an as-if/when-issued basis before the start of secondary trading. Using actual when-issued trades made by a sample of clients at a large German retail broker during 1999 and 2000, the paper documents that retail buyers consistently overpay for initial public offerings (IPOs) in the when-issued market relative to the immediate aftermarket. The observed willingness to overpay points to sentiment as a driver of retail trading decisions. Consistent with this interpretation and with sentiment affecting prices, IPOs that are aggressively bought by individuals in the when-issued market exhibit high first-day returns as well as poor aftermarket returns relative to benchmarks of similar stocks.
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Baglini, Rebekah Brita, Lasse Hansen, Kenneth Enevoldsen, and Kristoffer Laigaard Nielbo. "MULTILINGUAL SENTIMENT NORMALIZATION FOR SCANDINAVIAN LANGUAGES." Scandinavian Studies in Language 12, no. 1 (December 31, 2021): 50–64. http://dx.doi.org/10.7146/sss.v12i1.130068.
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In this paper, we address the challenge of multilingual sentiment analysis using a traditional lexicon and rule-based sentiment instrument that is tailored to capture sentiment patterns in a particular language. Focusing on a case study of three closely related Scandinavian languages (Danish, Norwegian, and Swedish) and using three tailored versions of VADER, we measure the relative degree of variation in valence using the OPUS corpus. We found that scores for Swedish are systematically skewed lower than Danish for translational pairs, and that scores for Norwegian are skewed higher for both other languages. We use a neural network to optimize the fit between Norwegian and Swedish respectively and Danish as the reference (target) language.
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Lang, Stephan, and Wolfgang Schaefers. "Examining the sentiment-return relationship in European real estate stock markets." Journal of European Real Estate Research 8, no. 1 (May 5, 2015): 24–45. http://dx.doi.org/10.1108/jerer-10-2014-0036.
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Purpose – Recent studies in the field of behavioral finance have highlighted the importance of investor sentiment in the return-generating process for general equities. By employing an asset pricing framework, this paper aims to evaluate the performance of European real estate equities, based on their degree of sentiment sensitivity. Design/methodology/approach – Using a pan-European data set, we classify all real estate equities according to their sentiment sensitivity, which is measured relative to the Economic Sentiment Indicator (ESI) of the European Commission. Based on their individual sentiment responsiveness, we form both a high- and low-sensitivity portfolio, whose returns are included in the difference test of the liquidity-augmented asset pricing model. In this context, we analyze the performance of sentiment-sensitive and sentiment-insensitive real estate equities with a risk-adjusted perspective over the period July 1995 to June 2012. Findings – While high-sensitivity real estate equities yield significantly higher raw returns than those with low-sensitivity, we find no evidence of risk-adjusted outperformance. This indicates that allegedly sentiment-driven return behavior is in fact merely compensation for taking higher fundamental risks. In this context, we find that sentiment-sensitive real estate equities are exposed to significantly higher market risks than sentiment-insensitive ones. Based on these findings, we conclude that a sentiment-based investment strategy, consisting of a long-position in the high-sensitivity portfolio and a short-position in the low-sensitivity one, does not generate a risk-adjusted profit. Research limitations/implications – Although this study sheds some light on investor sentiment in European real estate stock markets, further research could usefully concentrate on alternative sentiment proxies. Originality/value – This is the first study to disentangle the relationship between investor sentiment and European real estate stock returns.
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Hsiao, Jung-Lieh, Teng-Tsai Tu, and Mei-Chun Chen. "Factors Influencing Equity Return Correlations between China’s Pairs of A- and B-Share Markets: Effect of QFII’s Implementation." International Journal of Financial Research 8, no. 2 (February 28, 2017): 105. http://dx.doi.org/10.5430/ijfr.v8n2p105.
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This paper was intended to examine factors influencing the correlations between A- and B-shares of individual firms, and explore the effects of Qualified Foreign Institutional Investor’s (QFII) implementation on correlations. The empirical results show that interest rate differential, relative turnover rate, relative return volatility, and market sentiment had impacts on correlation both before and after the QFII’s implementation. After its implementation, correlations became more sensitive to premium, relative turnover rate and market sentiment. Furthermore, the estimated constant term for overall market correlation became more negative (raw values from -0.3413 to -0.8815), indicating an increasing correlation between A- and B-shares’ returns. The policy implications are that much benefit of diversification into emerging markets such as paired A-and B-shares can be accomplished, together with taking several influential factors into account.
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Pöferlein, Matthias. "Sentiment Analysis of German Texts in Finance: Improving and Testing the BPW Dictionary." Journal of Banking and Financial Economics 2021, no. 2(16) (December 30, 2021): 5–24. http://dx.doi.org/10.7172/2353-6845.jbfe.2021.2.1.
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Using the dictionary-based approach to measure the sentiment of finance-related texts is primarily focused on English-speaking content. This is due to the need for domain-specific dictionaries and the primary availability of those in English. Through the contribution of Bannier et al. (2019b), the first finance-related dictionary is available for the German language. Because of the novelty of this dictionary, this paper proposes several reforms and extensions of the original word lists. Additionally, I tested multiple measurements of sentiment. I show that using the edited and extended dictionary to calculate a relative measurement of sentiment, central assumptions regarding textual analysis can be fulfilled and more significant relations between the sentiment of a speech by a CEO at the Annual General Meeting and subsequent abnormal stock returns can be calculated.
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Meseguer, Covadonga, and Achim Kemmerling. "What Do You Fear? Anti-immigrant Sentiment in Latin America." International Migration Review 52, no. 1 (March 2018): 236–72. http://dx.doi.org/10.1111/imre.12269.
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In this article, we study the material determinants of anti-immigrant sentiment in Latin America. Based on new data on immigration to non-Organisation for Economic Co-operation and Development (OECD) countries, we use the workhorse distributive theories that anticipate who wins and who loses from immigration and test their predictive capacity in labor-abundant countries. We exploit the variation in regional immigration rates, in the skill composition of natives versus migrants, and in the relative generosity of Latin American welfare states. We find that fears of labor-market competition are weak predictors of anti-immigrant sentiment. In contrast, fears of greater tax burdens are strong and robust predictors of anti-immigrant sentiment. We conclude that studying Latin American public opinion opens new avenues for theorizing about anti-immigrant sentiment in developing countries.
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Miao, Yuqing, Ronghai Luo, Lin Zhu, Tonglai Liu, Wanzhen Zhang, Guoyong Cai, and Ming Zhou. "Contextual Graph Attention Network for Aspect-Level Sentiment Classification." Mathematics 10, no. 14 (July 15, 2022): 2473. http://dx.doi.org/10.3390/math10142473.
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Aspect-level sentiment classification aims to predict the sentiment polarities towards the target aspects given in sentences. To address the issues of insufficient semantic information extraction and high computational complexity of attention mechanisms in existing aspect-level sentiment classification models based on deep learning, a contextual graph attention network (CGAT) is proposed. The proposed model adopts two graph attention networks to aggregate syntactic structure information into target aspects and employs a contextual attention network to extract semantic information in sentence-aspect sequences, aiming to generate aspect-sensitive text features. In addition, a syntactic attention mechanism based on syntactic relative distance is proposed, and the Gaussian function is cleverly introduced as a syntactic weight function, which can reduce computational complexities and effectively highlight the words related to aspects in syntax. Experiments on three public sentiment datasets show that the proposed model can make better use of semantic information and syntactic structure information to improve the accuracy of sentiment classification.
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Manivannan, P., and C. S. Kanimozhi Selvi. "Pairwise relative ranking technique for efficient opinion mining using sentiment analysis." Cluster Computing 22, S6 (February 23, 2018): 13487–97. http://dx.doi.org/10.1007/s10586-018-1986-5.
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LLAMAS ROIG, Vicente. "Fragmentary Metaphysics of the Transcendentals in the Thought of Durandus of Saint-Pourçain." Revista Española de Filosofía Medieval 24 (November 24, 2017): 159. http://dx.doi.org/10.21071/refime.v24i.10457.
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Throughout scattered passages in Quodlibeta Avenionensia and the comments on Liber Sententiarum we can reconstruct the hardly explored core of some metaphysics of the transcendentals in the thought of Durandus of Saint-Pourçain. Bonum: an extrinsic denomination of the entity diverted from the distinctions of negotiantis et ratiocinantis reason, which would redefine evil as disconvenientia, without dismissing its definition as privation of good. The relative being of truth as the relationship of the thing with itself according to its intellective being and its effective real being, or the characterization of the transcendental unum as convenience of the esse individuum to that which exists per illud quod est with explicit veto on the condition of separable accident, outline an original collage, with diverse doctrinal elements, which condemns the dimension of absolute res or the positive formal matrix of the transcendentals.
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Tauseef, Sana. "Sentiment and Stock Returns: A Case for Conventional and Islamic equities in Pakistan." Business & Economic Review 12, no. 3 (September 15, 2020): 1–22. http://dx.doi.org/10.22547/ber/12.3.1.
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The study constructs market sentiment index over the period from August 2009 to June 2019 and examines the causality between market sentiment and returns for conventional and Islamic stocks in Pakistan. Using the firm-level data for all stocks listed on Pakistan Stock Exchange, market sentiment index is constructed as the first principal component of six variables: advances-to-decline, premium on dividends, price-to-earnings, relative strength, money flow and turnover rate. We employ the Vector auto-regression model to examine the two-way causal relationship between investor sentiment and aggregate stock return. Our results show that market sentiment has strong predictive power for subsequent conventional stock returns. Sentiment based trading actions of the investors cause persistence in conventional stock returns for one month; however, as these stocks become overpriced, the price movement reverses in two months’ time. In contrast, we do not find any significant association between market sentiment and Islamic stock returns. Our findings are suggestive of different dynamics and investor behavior in Islamic financial markets of Pakistan and along with the existing literature documenting Islamic stocks performance to be at least as good as the conventional stock can be a comfort to the Muslim Investors and may serve as the catalyst to stimulate the growth of Islamic equities.
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Maharjan, Subin, and Subarna Shakya. "Stock Index Prediction with Financial News Sentiments and Technical Indicators." Journal of ISMAC 4, no. 3 (September 19, 2022): 198–210. http://dx.doi.org/10.36548/jismac.2022.3.006.
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The price of a stock in the market can be influenced by many factors, out of which the sentiment of the investors plays a vital role. Most often, the sentiment of investors depends on the sentiment of the news headlines. Therefore, news headlines also play an important role in the fluctuation of the stock index. This paper uses the combination of Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional Gated Recurrent Unit (BiGRU) algorithms for the prediction of news sentiment scores based on national news headlines and financial news data. Technical indicators like Relative Strength Index (RSI), Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Stochastic Oscillator along with normal stock indicators like ’Date’, ’Open’, ’Close’, ’High’, ’Low’ and ’Volume’ data can be used to predict the short-term momentum of the stock value. This paper uses the BiGRU algorithm to predict the stock index value (a) with technical indicators only and (b) with technical indicators and news sentiment scores. Keeping all the hyperparameters constant, the BiGRU algorithm provided better prediction results when news sentiment scores were added to the dataset along with technical parameters as an input.
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Verlhiac, Jean-Francois. "Les effets du statut et de la privation relative sur l'optimisme comparatif de sujets de faibles ressources socio-économiques." Les cahiers internationaux de psychologie sociale Numéro 72, no. 4 (2006): 23. http://dx.doi.org/10.3917/cips.072.0023.
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Wall, Daniel, Raymond D. Crookes, Eric J. Johnson, and Elke U. Weber. "Risky choice frames shift the structure and emotional valence of internal arguments: A query theory account of the unusual disease problem." Judgment and Decision Making 15, no. 5 (September 2020): 685–703. http://dx.doi.org/10.1017/s1930297500007877.
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AbstractWe examine a Query Theory account of risky choice framing effects — when risky choices are framed as a gain, people are generally risky averse but, when an equivalent choice is framed as a loss, people are risk seeking. Consistent with Query Theory, frames affected the structure of participants’ arguments: gain frame participants listed arguments favoring the certain option earlier and more often than loss frame participants. These argumentative shifts mediated framing effects; manipulating participants initial arguments attenuated them. While emotions, as measured by PANAS, were related to frames but not related to choices, an exploratory text analysis of the affective valence of arguments was related to both. Compared to loss-frame participants, gain-frame participants expressed more positive sentiment towards the certain option than the risky option. This relative-sentiment index predicted choices by itself but not when included with structure of arguments. Further, manipulated initial arguments did not significantly affect participant’s relative sentiment. Prior to changing choices, risky choice frames alter both the structure and emotional valence of participants’ internal arguments.
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Unger, Stephan. "The Role of Country-pair-related News Sentiment in Foreign Exchange." Athens Journal of Business & Economics 9, no. 3 (June 29, 2023): 327–44. http://dx.doi.org/10.30958/ajbe.9-3-5.
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This article explores the relative, explanatory contribution of country-pair-related political and financial news to foreign exchange rates. Contributing political factors are measured through the sentiment scores of published news while contributing financial factors are measured through various economic indicators such as price and volume of USD and CNY oil futures, the Russian IMOEX Index, and corresponding interest differentials. The results show that news sentiment plays a minor role in exchange rate determination while other factors such as prices and traded volumes in oil future contracts and interest differentials are significant contributing factors to the exchange rate determination. Nevertheless, the quality and quantity of news coverage of geo-political or economic events seems to play an important role when it comes to the impact of news on exchange rates. Among the sentiment-analyzed currency pairs, EUR/USD exhibits by far the highest sensitivity to political and economic news, followed by EUR/RUB, RUB/CNY, EUR/CNY, USD/CNY, and USD/RUB. Keywords: foreign exchange, news sentiment analysis, text mining, geo-political sentiment JEL-Codes: F31, E71
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Chen, Wenwen. "Deep adversarial neural network model based on information fusion for music sentiment analysis." Computer Science and Information Systems, no. 00 (2023): 31. http://dx.doi.org/10.2298/csis221212031c.
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Natural language processing (NLP) is a computer-based technology used to process natural language information in written and spoken form that is unique to human society. In the process of mining massive text information, a variety of technologies and research directions in the field of NLP have gradually emerged. And sentiment analysis is an important research direction, which has important research value and practical application value for enterprises and social life. Sentiment analysis is basically a single mining of semantic or grammatical information without establishing the correlation between semantic information and grammatical information. In addition, previous models simply embed the relative distance or grammatical distance of words into the model, ignoring the joint influence of relative distance and grammatical distance on the aspect words. In this paper, we propose a new model that combines deep adversarial neural network model based on information fusion for music sentiment analysis. Firstly, the information of music text sequence is captured by the bidirectional short and long time memory network. Then the sequence information is updated according to the tree structure of dependency syntactic tree. Then, the relative distance and syntactic distance position information are embedded into the music text sequence. Thirdly, the adversarial training is used to expand the alignment boundary of the field distribution and effectively alleviate the problem of fuzzy features leading to misclassification. Semantic information and syntactic information are optimized by attention mechanism. Finally, the fused information is input into the Softmax classifier for music sentiment classification. Experimental results on open data sets show that compared with other advanced methods, the recognition accuracy of the proposed method is more than 90%.
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Moreo, Alejandro, and Fabrizio Sebastiani. "Tweet sentiment quantification: An experimental re-evaluation." PLOS ONE 17, no. 9 (September 16, 2022): e0263449. http://dx.doi.org/10.1371/journal.pone.0263449.
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Sentiment quantification is the task of training, by means of supervised learning, estimators of the relative frequency (also called “prevalence”) of sentiment-related classes (such as Positive, Neutral, Negative) in a sample of unlabelled texts. This task is especially important when these texts are tweets, since the final goal of most sentiment classification efforts carried out on Twitter data is actually quantification (and not the classification of individual tweets). It is well-known that solving quantification by means of “classify and count” (i.e., by classifying all unlabelled items by means of a standard classifier and counting the items that have been assigned to a given class) is less than optimal in terms of accuracy, and that more accurate quantification methods exist. Gao and Sebastiani 2016 carried out a systematic comparison of quantification methods on the task of tweet sentiment quantification. In hindsight, we observe that the experimentation carried out in that work was weak, and that the reliability of the conclusions that were drawn from the results is thus questionable. We here re-evaluate those quantification methods (plus a few more modern ones) on exactly the same datasets, this time following a now consolidated and robust experimental protocol (which also involves simulating the presence, in the test data, of class prevalence values very different from those of the training set). This experimental protocol (even without counting the newly added methods) involves a number of experiments 5,775 times larger than that of the original study. Due to the above-mentioned presence, in the test data, of samples characterised by class prevalence values very different from those of the training set, the results of our experiments are dramatically different from those obtained by Gao and Sebastiani, and provide a different, much more solid understanding of the relative strengths and weaknesses of different sentiment quantification methods.
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Ljajic, Adela, and Ulfeta Marovac. "Improving sentiment analysis for twitter data by handling negation rules in the Serbian language." Computer Science and Information Systems 16, no. 1 (2019): 289–311. http://dx.doi.org/10.2298/csis180122013l.
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The importance of determining sentiment for short text increases with the rise in the number of comments on social networks. The presence of negation in these texts affects their sentiment, because it has a greater range of action in proportion to the length of the text. In this paper, we examine how the treatment of negation impacts the sentiment of tweets in the Serbian language. The grammatical rules that influence the change of polarity are processed. We performed an analysis of the effect of the negation treatment on the overall process of sentiment analysis. A statistically significant relative improvement was obtained (up to 31.16% or up to 2.65%) when the negation was processed using our rules with the lexicon-based approach or machine learning methods. By applying machine learning methods, an accuracy of 68.84% was achieved on a set of positive, negative and neutral tweets, and an accuracy of as much as 91.13% when applied to the set of positive and negative tweets.
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Wang, Xiaodi, Xiaoliang Chen, Mingwei Tang, Tian Yang, and Zhen Wang. "Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism." Discrete Dynamics in Nature and Society 2020 (July 8, 2020): 1–13. http://dx.doi.org/10.1155/2020/5824873.
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The aim of aspect-level sentiment analysis is to identify the sentiment polarity of a given target term in sentences. Existing neural network models provide a useful account of how to judge the polarity. However, context relative position information for the target terms is adversely ignored under the limitation of training datasets. Considering position features between words into the models can improve the accuracy of sentiment classification. Hence, this study proposes an improved classification model by combining multilevel interactive bidirectional Gated Recurrent Unit (GRU), attention mechanisms, and position features (MI-biGRU). Firstly, the position features of words in a sentence are initialized to enrich word embedding. Secondly, the approach extracts the features of target terms and context by using a well-constructed multilevel interactive bidirectional neural network. Thirdly, an attention mechanism is introduced so that the model can pay greater attention to those words that are important for sentiment analysis. Finally, four classic sentiment classification datasets are used to deal with aspect-level tasks. Experimental results indicate that there is a correlation between the multilevel interactive attention network and the position features. MI-biGRU can obviously improve the performance of classification.
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Jahić, Sead, and Jernej Vičič. "Impact of Negation and AnA-Words on Overall Sentiment Value of the Text Written in the Bosnian Language." Applied Sciences 13, no. 13 (June 30, 2023): 7760. http://dx.doi.org/10.3390/app13137760.
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In this manuscript, we present our efforts to develop an accurate sentiment analysis model for Bosnian-language tweets which incorporated three elements: negation cues, AnA-words (referring to maximizers, boosters, approximators, relative intensifiers, diminishers, and minimizers), and sentiment-labeled words from a lexicon. We used several machine-learning techniques, including SVM, Naive Bayes, RF, and CNN, with different input parameters, such as batch size, number of convolution layers, and type of convolution layers. In addition to these techniques, BOSentiment is used to provide an initial sentiment value for each tweet, which is then used as input for CNN. Our best-performing model, which combined BOSentiment and CNN with 256 filters and a size of 4×4, with a batch size of 10, achieved an accuracy of over 92%. Our results demonstrate the effectiveness of our approach in accurately classifying the sentiment of Bosnian tweets using machine-learning techniques, lexicons, and pre-trained models. This study makes a significant contribution to the field of sentiment analysis for under-researched languages such as Bosnian, and our approach could be extended to other languages and social media platforms to gain insight into public opinion.
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Cao Mai Phuong, Lai, and Vu Cam Nhung. "Investor sentiment measurement based on technical analysis indicators affecting stock returns: Empirical evidence on VN100." Investment Management and Financial Innovations 18, no. 4 (December 3, 2021): 297–308. http://dx.doi.org/10.21511/imfi.18(4).2021.25.
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The purpose of this study is to examine whether investor sentiment as measured by technical analysis indicators has an impact on stock returns. The research period is from 2015 to mid-2020. 1-year government bond yields, financial data, transaction data of 57 companies in the VN100 basket, and VNIndex are analyzed. The investor sentiment variable is measured by each technical analysis indicator (Relative Strength Index – RSI, Psychological Line Index – PLI), and the general sentiment variable is established based on extracting the principal component from individual indicators. The paper uses two regression methods – Fama-MacBeth and Generalized Least Square (GLS) – for five different research models. The results show that sentiment plays an important role in stock returns in the Vietnamese stock market. Even controlling the factors such as cash flow per share, firm size, market risk premium, and stock price volatility in the studied models, the impact of sentiment is significant in both the model using individual technical indicators and the model using the general sentiment variable. Furthermore, investor sentiment has a stronger power to explain excess stock returns than their trading behavior. The implication from the results shows that the Vietnamese stock market is inefficient, in which psychology is a very important issue and participants need to pay due attention to this factor. AcknowledgmentThis study was funded by the Industrial University of Ho Chi Minh City (IUH), Vietnam (grant number: 21/1TCNH03).
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Jiang, Julie, Nils Murrugara-Llerena, Maarten W. Bos, Yozen Liu, Neil Shah, Leonardo Neves, and Francesco Barbieri. "Sunshine with a Chance of Smiles: How Does Weather Impact Sentiment on Social Media?" Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 393–404. http://dx.doi.org/10.1609/icwsm.v16i1.19301.
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The environment we are in can affect our mood and behavior. One environmental factor is weather, which is linked to sentiment as expressed on social media. However, less is known about how integrating changes in weather, along with time and location contextual cues, can improve sentiment detection and understanding. In this paper, we explore the effects of three contextual features--weather, location, and time--on expressed sentiment in social media. Leveraging a large Snapchat dataset, we provide extensive experimental evidence that including contextual features in addition to textual features significantly improves textual sentiment detection performance by 3% over transformer-based language models. Our results also generalize cross-domain to Twitter. Ablation studies indicate the relative importance of weather compared to location and time. We also conduct correlation analyses on 8 million Snapchat posts to highlight the link between past weather and current sentiment, showing that weather has a lasting impact on mood. Users generally exhibit more positive sentiment in better weather conditions as well as in improved weather conditions. Additionally, we show that temperature's link with mood holds after controlling for time or population density, but there exist geographical differences in how temperature affects mood. Our work demonstrates the effectiveness of including external contexts in linguistic tasks and carries design implications for researchers and designers of social media.
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Wulandari, Sari, Ghina Atha, Putra Fajar Alam, and Meldi Rendra. "Identification of Karleen Hijab Fashion SME Competitors Based on Sentiment Analysis Using Naïve Bayes Classifier Algorithm." JTERA (Jurnal Teknologi Rekayasa) 7, no. 2 (December 31, 2022): 323. http://dx.doi.org/10.31544/jtera.v7.i2.2022.323-330.
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Hijab Fashion Small and Medium-Sized Enterprises (SMEs) need to develop competitive advantages brands as a source of SME competitiveness. However, most Hijab Fashion SMEs experience limitations in developing the competitive advantages of their brands. This research was conducted to find out and understand the competitive advantages of Karleen Hijab Fashion SME competitors as the object of study. The method used is sentiment analysis using the Naïve Bayes algorithm. Sentiment analysis was carried out using online review data of Shopee e-commerce. Sentiment analysis data processing was done using orange data mining software. Sentiment analysis using the Naïve Bayes algorithm produced an average value of AUC, CA, F1, Precision and adequate recall for the entire Hijab Fashion SME brand, which is 0.72, 0.887, 0.856, 0.833, and 0.887. Based on the percentage of the largest positive sentiment on each fashion quality attribute, it is known that competitive advantages of Lozy are in the Fabric Quality Attribute (30.77%), and Good Fit (15.38%), and Halwa's competitive advantage is in the Design attribute (34.19%). Competitive advantages of Hijup are on the Serviceability Attribute (21.74%) and Packaging (15.38%), and Competitive advantages of Lafiye are on the Price Attribute (6.17%). Competitive advantages of Deenay brand are on the Reliability Attribute (20.89%), while Karleen does not have a relative advantage on any fashion quality attribute because the percentage of positive sentiment for each attribute is still below competitors.
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Singh, Amit, Mamata Jenamani, Jitesh Thakkar, and Yogesh K. Dwivedi. "A Text Analytics Framework for Performance Assessment and Weakness Detection From Online Reviews." Journal of Global Information Management 30, no. 8 (September 1, 2021): 1–26. http://dx.doi.org/10.4018/jgim.304069.
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Present research proposes a framework that integrates aspect-level sentiment analysis with multi-criteria decision making (TOPSIS) and control charts to uncover hidden quality patterns. While sentiment analysis quantifies consumer opinions corresponding to various product features, TOPSIS uses the sentiment scores to rank manufacturers based on their relative performance. Finally, U and P control charts assist in discovering the weak aspects and corresponding attributes. To extract aspect-level sentiments from reviews, we developed the ontology of passenger cars and designed a heuristic that connects the opinion-bearing texts to the exact automobile attribute. The proposed framework was applied to a review dataset collected from a well-known car portal in India. Considering five manufacturers from the mid-size car segment, we identified the weakest and discovered the aspects and attributes responsible for its perceived weakness.
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Hayward, Rob, and Andros Gregoriou. "International Capital Flows and Speculation." Journal of Risk and Financial Management 14, no. 5 (April 29, 2021): 197. http://dx.doi.org/10.3390/jrfm14050197.
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In response to questions about the relative importance of different types of capital flow for international competitiveness, we develop a structural vector auto-regressive model of the real exchange rate and international capital flows. We reveal that innovations to speculative sentiment cause changes in competitiveness. We report that speculation replaces the effect of equity, bond and most of the interest rate effect. The results show that international speculative sentiment is an important contributor to exchange rate and that monetary and regulatory authorities should find ways of measuring and understanding banking and financial flows.
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Zhou, Xueli. "Sentiment Analysis of the Consumer Review Text Based on BERT-BiLSTM in a Social Media Environment." International Journal of Information Technologies and Systems Approach 16, no. 2 (July 11, 2023): 1–16. http://dx.doi.org/10.4018/ijitsa.325618.
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In this paper, a BERT-BiLSTM-based consumer review text sentiment analysis method in the e-commerce big data field is proposed. First, the unlabeled text is trained using the BERT training model for the language introduced in the deep learning, and then the pre-training model of the text data is delivered by the learning textual features and data to extract deeper vectors. Second, the BiLSTM model is applied to simultaneously obtain contextual information so as to illustrate optimal textual features. Finally, a corresponding sentiment analysis model relative to the consumer review text is constructed by combining the BERT model with BiLSTM to better merge the context for classifying sentiment and improving the final feature vector accuracy for the sentiment classification results. Simulated by experiments, the method proposed in this paper was compared with another three methods using the same data set. The results obtained indicate that the proposed method has the highest precision, recall, and F1-Measure, and the values reach 92.64%, 90.32%, and 91.46%, respectively.
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Seydou Hanafiou, Hamidou. "Représentations linguistiques des locuteurs natifs du songhay-zarma." Cahiers du Centre de Linguistique et des Sciences du Langage, no. 15 (April 9, 2022): 185–205. http://dx.doi.org/10.26034/la.cdclsl.2004.1632.
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Au-delà des travaux des spécialistes et des appellations officielles, on doit aussi souligner l'attitude des locuteurs - Songhay (ou Kaado), Zarma et Dendi - qui manifestent le sentiment de parler la même langue. Ce sentiment tient certainement à l'intercompréhension d'une part, mais aussi à l'histoire commune. Ayant eu l'occasion de participer nous-même à la production des données, nous avons noté des réactions d'enquêtés le mettant en évidence. En témoignent les réponses à la question relative à la LI : il est arrivé qu'un locuteur du kaado (ou songhay) dise parler zarma. Même si le nombre de personnes de l'aire zarma qui se sont déclarées zarmaphones est supérieur à celui des Songhay qui se disent songhayophones, le fait qu'un pourcentage, même faible, d'un sous-groupe utilise le terme qui a trait à l'autre sous-groupe traduit le sentiment de parler la même langue.
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Moore, Michael Thomas. "Constructing a sentiment analysis model for LibQUAL+ comments." Performance Measurement and Metrics 18, no. 1 (April 10, 2017): 78–87. http://dx.doi.org/10.1108/pmm-07-2016-0031.
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Purpose The purpose of this paper is to establish a data mining model for performing sentiment analysis on open-ended qualitative LibQUAL+ comments, providing a further method for year-to-year comparison of user satisfaction, both of the library as a whole and individual topics. Design/methodology/approach A training set of 514 comments, selected at random from five LibQUAL+ survey responses, was manually reviewed and labeled as having a positive or negative sentiment. Using the open-source RapidMiner data mining platform, those comments provided the framework for creating library-specific positive and negative word vectors to power the sentiment analysis model. A further process was created to help isolate individual topics within the larger comments, allowing for more nuanced sentiment analysis. Findings Applied to LibQUAL+ comments for a Canadian mid-sized academic research library, the model suggested a fairly even distribution of positive and negative sentiment in overall comments. When filtering comments into affect of service, information control and library as place, the three dimensions’ relative polarity mirrored the results of the quantitative LibQUAL+ questions, with highest scores for affect of service and lowest for library as place. Practical implications The sentiment analysis model provides a complementary tool to the LibQUAL+ quantitative results, allowing for simple, time-efficient, year-to-year analysis of open-ended comments. Furthermore, the process provides the means to isolate specific topics based on specified keywords, allowing individual institutions to tailor results for more in-depth analysis. Originality/value To best account for library-specific terminology and phrasing, the sentiment model was created using LibQUAL+ open-ended comments as the foundation for the sentiment model’s classification process. The process also allows individual topics, chosen to meet individual library needs, to be isolated and independently analyzed, providing more precise examination.