Relevant bibliographies by topics / POLARITY DATASET

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'POLARITY DATASET'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "POLARITY DATASET"

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Tian, Jing, Wushour Slamu, Miaomiao Xu, Chunbo Xu, and Xue Wang. "Research on Aspect-Level Sentiment Analysis Based on Text Comments." Symmetry 14, no. 5 (May 23, 2022): 1072. http://dx.doi.org/10.3390/sym14051072.

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Sentiment analysis is the processing of textual data and giving positive or negative opinions to sentences. In the ABSA dataset, most sentences contain one aspect of sentiment polarity, or sentences of one aspect have multiple identical sentiment polarities, which weakens the sentiment polarity of the ABSA dataset. Therefore, this paper uses the SemEval 14 Restaurant Review dataset, in which each document is symmetrically divided into individual sentences, and two versions of the datasets ATSA and ACSA are created. ATSA: Aspect Term Sentiment Analysis Dataset. ACSA: Aspect Category Sentiment Analysis Dataset. In order to symmetrically simulate the complex relationship between aspect contexts and accurately extract the polarity of emotional features, this paper combines the latest development trend of NLP, combines capsule network and BRET, and proposes the baseline model CapsNet-BERT. The experimental results verify the effectiveness of the model.
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Abdullah Haje, Umran, Mohammed Hussein Abdalla, Reben Mohammed Saleem Kurda, and Zhwan Mohammed Khalid. "A New Model for Emotions Analysis in Social Network Text Using Ensemble Learning and Deep learning." Academic Journal of Nawroz University 11, no. 1 (March 9, 2022): 130–40. http://dx.doi.org/10.25007/ajnu.v11n1a1250.

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Recently, emotion analysis has become widely used. Therefore, increasing the accuracy of existing methods has become a challenge for researchers. The proposed method in this paper is a hybrid model to improve the accuracy of emotion analysis; Which uses a combination of convolutional neural network and ensemble learning. In the proposed method, after receiving the dataset, the data is pre-processed and converted into process able samples. Then the new dataset is split into two categories of training and test. The proposed model is a structure for machine learning in the form of ensemble learning. It contains blocks consisting of a combination of convolutional networks and basic classification algorithms. In each convolutional network, the base classification algorithms replace the fully connected layer. Evaluate the proposed method, in IMDB, PL04 and SemEval dataset with accuracy, precision, recall and F1 criteria, shows that, on average, for all three datasets, the precision of polarity detection is 90%, the recall of polarity detection is 93%, the F1 of polarity detection is 91% and finally the accuracy of polarity detection is 92%.
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Al-Kabi, Mohammed N., Heider A. Wahsheh, and Izzat M. Alsmadi. "Polarity Classification of Arabic Sentiments." International Journal of Information Technology and Web Engineering 11, no. 3 (July 2016): 32–49. http://dx.doi.org/10.4018/ijitwe.2016070103.

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Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.
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Jung, Soon-Gyo, Joni Salminen, and Bernard J. Jansen. "Engineers, Aware! Commercial Tools Disagree on Social Media Sentiment: Analyzing the Sentiment Bias of Four Major Tools." Proceedings of the ACM on Human-Computer Interaction 6, EICS (June 14, 2022): 1–20. http://dx.doi.org/10.1145/3532203.

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Large commercial sentiment analysis tools are often deployed in software engineering due to their ease of use. However, it is not known how accurate these tools are, and whether the sentiment ratings given by one tool agree with those given by another tool. We use two datasets - (1) NEWS consisting of 5,880 news stories and 60K comments from four social media platforms: Twitter, Instagram, YouTube, and Facebook; and (2) IMDB consisting of 7,500 positive and 7,500 negative movie reviews - to investigate the agreement and bias of four widely used sentiment analysis (SA) tools: Microsoft Azure (MS), IBM Watson, Google Cloud, and Amazon Web Services (AWS). We find that the four tools assign the same sentiment on less than half (48.1%) of the analyzed content. We also find that AWS exhibits neutrality bias in both datasets, Google exhibits bi-polarity bias in the NEWS dataset but neutrality bias in the IMDB dataset, and IBM and MS exhibit no clear bias in the NEWS dataset but have bi-polarity bias in the IMDB dataset. Overall, IBM has the highest accuracy relative to the known ground truth in the IMDB dataset. Findings indicate that psycholinguistic features - especially affect, tone, and use of adjectives - explain why the tools disagree. Engineers are urged caution when implementing SA tools for applications, as the tool selection affects the obtained sentiment labels.
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Tripathy, Abinash, and Santanu Kumar Rath. "Classification of Sentiment of Reviews using Supervised Machine Learning Techniques." International Journal of Rough Sets and Data Analysis 4, no. 1 (January 2017): 56–74. http://dx.doi.org/10.4018/ijrsda.2017010104.

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Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative or neutral. It is observed that very often the data associated with the sentiment analysis consist of the feedback given by various specialists on any topic or product. Thus, the review may be categorized properly into any sort of class based on the polarity, in order to have a good knowledge about the product. This article proposes an approach to classify the review dataset made on basis of sentiment analysis into different polarity groups. Four machine learning algorithms viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA) have been considered in this paper for classification process. The obtained result on values of accuracy of the algorithms are critically examined by using different performance parameters, applied on two different datasets.
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Kuriyozov, Elmurod, and Sanatbek Matlatipov. "Building a New Sentiment Analysis Dataset for Uzbek Language and Creating Baseline Models." Proceedings 21, no. 1 (August 2, 2019): 37. http://dx.doi.org/10.3390/proceedings2019021037.

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Making natural language processing technologies available for low-resource languages is an important goal to improve the access to technology in their communities of speakers. In this paper, we provide the first annotated corpora for polarity classification for Uzbek language. Our methodology considers collecting a medium-size manually annotated dataset and a larger-size dataset automatically translated from existing resources. Then, we use these datasets to train sentiment analysis models on the Uzbek language, using both traditional machine learning techniques and recent deep learning models.
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Pecar, Samuel, Tobias Daudert, and Marian Simko. "Evaluation of end-to-end aspect-based sentiment analysis methods employing novel benchmark dataset for aspect, and opinion review analysis." Intelligent Data Analysis 26, no. 6 (November 12, 2022): 1617–41. http://dx.doi.org/10.3233/ida-216252.

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Aspect-based sentiment analysis (ABSA) deals with the determination of sentiments for opinion targets. While historically this research task has been addressed with pipeline approaches, more recent works use neural networks to jointly deal with the aspect term and opinion term extraction, as well as the polarity classification. Although learned together, most NN-based approaches and all pipeline approaches do not model correlations between the tasks. This is also based on the absence of adequate datasets which are annotated for all sub-tasks in a unified tagging scheme. We address this bottleneck and introduce the first purposely designed and annotated dataset for ABSA. The DAORA dataset covers 2,100 Tripadvisor reviews, and it is annotated on aspect terms, opinion terms, as well as aspect term polarity, using a unified tagging scheme. It was designed especially for end-to-end aspect-based sentiment analysis of real-world reviews and does not use any sentence repetition or removal. We evaluate the DAORA dataset in several experiments employing state-of-the-art models for ABSA. We set benchmarks and analyze the strengths as well as weaknesses of the data and approaches.
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Kouadri, Wissam Mammar, Mourad Ouziri, Salima Benbernou, Karima Echihabi, Themis Palpanas, and Iheb Ben Amor. "Quality of sentiment analysis tools." Proceedings of the VLDB Endowment 14, no. 4 (December 2020): 668–81. http://dx.doi.org/10.14778/3436905.3436924.

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In this paper, we present a comprehensive study that evaluates six state-of-the-art sentiment analysis tools on five public datasets, based on the quality of predictive results in the presence of semantically equivalent documents, i.e., how consistent existing tools are in predicting the polarity of documents based on paraphrased text. We observe that sentiment analysis tools exhibit intra-tool inconsistency , which is the prediction of different polarity for semantically equivalent documents by the same tool, and inter-tool inconsistency , which is the prediction of different polarity for semantically equivalent documents across different tools. We introduce a heuristic to assess the data quality of an augmented dataset and a new set of metrics to evaluate tool inconsistencies. Our results indicate that tool inconsistencies is still an open problem, and they point towards promising research directions and accuracy improvements that can be obtained if such inconsistencies are resolved.
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Alghazzawi, Daniyal M., Anser Ghazal Ali Alquraishee, Sahar K. Badri, and Syed Hamid Hasan. "ERF-XGB: Ensemble Random Forest-Based XG Boost for Accurate Prediction and Classification of E-Commerce Product Review." Sustainability 15, no. 9 (April 23, 2023): 7076. http://dx.doi.org/10.3390/su15097076.

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Recently, the concept of e-commerce product review evaluation has become a research topic of significant interest in sentiment analysis. The sentiment polarity estimation of product reviews is a great way to obtain a buyer’s opinion on products. It offers significant advantages for online shopping customers to evaluate the service and product qualities of the purchased products. However, the issues related to polysemy, disambiguation, and word dimension mapping create prediction problems in analyzing online reviews. In order to address such issues and enhance the sentiment polarity classification, this paper proposes a new sentiment analysis model, the Ensemble Random Forest-based XG boost (ERF-XGB) approach, for the accurate binary classification of online e-commerce product review sentiments. Two different Internet Movie Database (IMDB) datasets and the Chinese Emotional Corpus (ChnSentiCorp) dataset are used for estimating online reviews. First, the datasets are preprocessed through tokenization, lemmatization, and stemming operations. The Harris hawk optimization (HHO) algorithm selects two datasets’ corresponding features. Finally, the sentiments from online reviews are classified into positive and negative categories regarding the proposed ERF-XGB approach. Hyperparameter tuning is used to find the optimal parameter values that improve the performance of the proposed ERF-XGB algorithm. The performance of the proposed ERF-XGB approach is analyzed using evaluation indicators, namely accuracy, recall, precision, and F1-score, for different existing approaches. Compared with the existing method, the proposed ERF-XGB approach effectively predicts sentiments of online product reviews with an accuracy rate of about 98.7% for the ChnSentiCorp dataset and 98.2% for the IMDB dataset.
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Zhao, Runcong, Lin Gui, Hanqi Yan, and Yulan He. "Tracking Brand-Associated Polarity-Bearing Topics in User Reviews." Transactions of the Association for Computational Linguistics 11 (2023): 404–18. http://dx.doi.org/10.1162/tacl_a_00555.

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Abstract Monitoring online customer reviews is important for business organizations to measure customer satisfaction and better manage their reputations. In this paper, we propose a novel dynamic Brand-Topic Model (dBTM) which is able to automatically detect and track brand-associated sentiment scores and polarity-bearing topics from product reviews organized in temporally ordered time intervals. dBTM models the evolution of the latent brand polarity scores and the topic-word distributions over time by Gaussian state space models. It also incorporates a meta learning strategy to control the update of the topic-word distribution in each time interval in order to ensure smooth topic transitions and better brand score predictions. It has been evaluated on a dataset constructed from MakeupAlley reviews and a hotel review dataset. Experimental results show that dBTM outperforms a number of competitive baselines in brand ranking, achieving a good balance of topic coherence and uniqueness, and extracting well-separated polarity-bearing topics across time intervals.1
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Dissertations / Theses on the topic "POLARITY DATASET"

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YADAV, DEEPIKA. "SENTIMENT ANALYSIS ON TWITTER DATA." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18821.

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Prior to purchasing an item, individuals for the most part go to different shops in the market, question about the item, cost, and guarantee, and afterward at long last purchase the item dependent on the feelings they got on cost and nature of administration. This procedure is tedious and the odds of being cheated by the merchant are more as there is no one to direct regarding where the purchaser can get valid item and with legitimate expense. Be that as it may, presently a-days a decent number of people rely upon the upon line showcase for purchasing their necessary items. This is on the grounds that the data about the items is accessible from numerous sources; in this manner, it is relatively modest and furthermore has the office of home conveyance. Once more, before experiencing the way toward setting request for any item, clients all the time allude to the remarks or audits of the current clients of the item, which assist them with taking choice about the nature of the item just as the administration gave by the dealer. Like putting request for items, it is seen that there are many experts in the field of films, who experience the film and afterward at long last give a remark about the nature of the film, i.e., to watch the film or not or in five-star rating. These audits are basically in the content arrangement and at times extreme to comprehend. In this manner, these reports should be prepared suitably to get some important data. Order of these audits is one of the ways to deal with extricate information about the surveys. In this theory, distinctive AI procedures are utilized to characterize the audits. Reproduction and trials are done to assess the exhibition of the proposed grouping strategies. It is seen that a decent number of scientists have frequently thought to be two distinctive survey datasets for conclusion grouping to be specific ascension and Polarity dataset. The IMDb dataset is separated into preparing and testing information. Accordingly, preparing information are utilized for preparing the AI calculations and testing information are utilized to test the information dependent on the preparation data. Then again, extremity dataset doesn't have separate information for preparing and testing. In this way, k-crease cross approval procedure is utilized to order the surveys. Four diverse AI strategies (MLTs) viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), and Linear Discriminant Analysis (LDA) are utilized for the order of these film audits. Diverse execution assessment boundaries are utilized to assess the presentation of the AI strategies. It is seen that among the over four AI calculations, RF method yields the grouping result, with more precision. Also, n-gram based characterization of surveys is completed on the ascension dataset. v The distinctive n-gram procedures utilized are unigram, bigram, trigram, unigram bigram, bigram + trigram, unigram + bigram + trigram. Four distinctive AI strategies, for example, Naive Bayes (NB), Maximum Entropy (ME), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD) methods are utilized to arrange the film surveys dependent on the n-gram strategy as referenced before. Diverse execution assessment boundaries are utilized to assess the presentation of these AI methods. The SVM method with unigram + bigram approach has demonstrated more exact outcome among every different methodologies. Thirdly, SVM-based element determination strategy is utilized to choose best highlights from the arrangement everything being equal. These chose highlights are then considered as contribution to Artificial Neural Network (ANN) to characterize the surveys information. For this situation, two distinctive audit datasets i.e., IMDb and Polarity dataset are considered for grouping. In this technique, each expression of these surveys is considered as a component, and the assumption estimation of each word is determined. The component choice is done dependent on the opinion estimations of the expression. The words having higher assumption esteems are chosen. These words at that point go about as a contribution to ANN based on which the film audits are ordered. At last, Genetic Algorithm (GA) is utilized to speak to the film surveys as chromosomes. Various activities of GA are completed to get the last arrangement result. Alongside this, the GA is likewise utilized as highlight choice to choose the best highlights from the arrangement of all highlights which in the end are given as contribution to ANN to acquire the last grouping outcome. Distinctive execution assessment boundaries are utilized to assess the presentation of GA and half breed of GA with ANN. Feeling examination regularly manages investigation of surveys, remarks about any item, which are for the most part printed in nature and need legitimate preparing to got any significant data. In this postulation, various methodologies have been proposed to arrange the audits into particular extremity gatherings, i.e., positive and negative. Distinctive MLTs are utilized in this theory to play out the errand of arrangement and execution of every strategy is assessed by utilizing various boundaries, viz., exactness, review, f-measure and precision. The outcomes acquired by the proposed approaches are seen as better than the outcomes as announced by different creators in writing utilizing same dataset and approaches.
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Book chapters on the topic "POLARITY DATASET"

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Apoorva, G. Drushti, and Radhika Mamidi. "BolLy: Annotation of Sentiment Polarity in Bollywood Lyrics Dataset." In Communications in Computer and Information Science, 41–50. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8438-6_4.

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Djebbi, Mohamed Amine, and Riadh Ouersighni. "TunTap: A Tunisian Dataset for Topic and Polarity Extraction in Social Media." In Computational Collective Intelligence, 507–19. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16014-1_40.

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Shaila, S. G., M. S. M. Prasanna, Shazia, C. Bhavya Shree, S. Arya, and K. P. Deshpande. "Polarity Classification of Sarcastic Sentence Patterns Based on N-Gram Technique for Twitter Dataset." In Lecture Notes in Networks and Systems, 239–47. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1559-8_25.

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Van Thin, Dang, Duc-Vu Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen, and Anh Hoang-Tu Nguyen. "Multi-task Learning for Aspect and Polarity Recognition on Vietnamese Datasets." In Communications in Computer and Information Science, 169–80. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6168-9_15.

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Tripathy, Abinash, and Santanu Kumar Rath. "Classification of Sentiment of Reviews using Supervised Machine Learning Techniques." In Cognitive Analytics, 143–63. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch009.

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Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative or neutral. It is observed that very often the data associated with the sentiment analysis consist of the feedback given by various specialists on any topic or product. Thus, the review may be categorized properly into any sort of class based on the polarity, in order to have a good knowledge about the product. This article proposes an approach to classify the review dataset made on basis of sentiment analysis into different polarity groups. Four machine learning algorithms viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA) have been considered in this paper for classification process. The obtained result on values of accuracy of the algorithms are critically examined by using different performance parameters, applied on two different datasets.
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Kumari, Suman, Basant Agarwal, and Mamta Mittal. "A Deep Neural Network Model for Cross-Domain Sentiment Analysis." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, 157–73. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6303-1.ch008.

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Sentiment analysis is used to detect the opinion/sentiment expressed from the unstructured text. Most of the existing state-of-the-art methods are based on supervised learning, and therefore, a labelled dataset is required to build the model, and it is very difficult task to obtain a labelled dataset for every domain. Cross-domain sentiment analysis is to develop a model which is trained on labelled dataset of one domain, and the performance is evaluated on another domain. The performance of such cross-domain sentiment analysis is still very limited due to presence of many domain-related terms, and the sentiment analysis is a domain-dependent problem in which words changes their polarity depending upon the domain. In addition, cross-domain sentiment analysis model suffers with the problem of large number of out-of-the-vocabulary (unseen words) words. In this paper, the authors propose a deep learning-based approach for cross-domain sentiment analysis. Experimental results show that the proposed approach improves the performance on the benchmark dataset.
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Muñoz-Chávez, J. Patricia, Rigoberto García-Contreras, and David Valle-Cruz. "Panic Station." In Advances in Marketing, Customer Relationship Management, and E-Services, 51–73. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4168-8.ch003.

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The COVID-19 pandemic led to changes in consumer behavior, where social commerce played a relevant role. Through the theory of protection motivation as a theoretical basis, this chapter´s purpose is the analysis of consumer sentiment in the evolution of panic buying for which the authors identified the trend themes and some important influencers during the contingency. The results show that the leaders with the highest positive sentiment levels were the President of Taiwan and the Prime Minister of Australia. WHO was the influential account with the most negative sentiment during the pandemic. Relative to trending topics, the dataset with the highest positive sentiment is related to cleaning and disinfection products. The face mask data set had the highest negative sentiment and is the trending topic with the highest polarity. The trending topic on health foods, vitamins, and food supplements had the lowest polarity.
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Sprugnoli, Rachele. "MultiEmotions-It: a New Dataset for Opinion Polarity and Emotion Analysis for Italian." In Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020, 402–8. Accademia University Press, 2020. http://dx.doi.org/10.4000/books.aaccademia.8910.

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Mehla, Stuti, and Sanjeev Rana. "An Optimized System for Sentiment Analysis using Twitter Data." In Challenges and Opportunities for Deep Learning Applications in Industry 4.0, 159–80. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9789815036060122010010.

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Progression in technology and innovation increases internet users, who post their perspectives on social media platforms regarding any product or service. It brings forth significant terms, i.e.,”feedback of users,” termed as sentiments and plays a substantial role for commercial organizations to analyze and find polarity related to their respective services. In Sentiment Analysis, the feature extraction phase is a crucial one that affects the entire process's processing. In the case of high dimensional Real- Time data, it leads to a sparse feature matrix and gives rise to steady processing. In this exploration work, we have proposed an Improved Optimized Feature Sentiment Classifier for Big Data (IOFSCBD) System, which deals with advancing the classifiers by giving improved values in each sort of dataset. Results show better execution of the Improved Optimized Feature Sentiment Classifier for Big Data system System.
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Jiménez-Ruano, Adrián, Pere Joan Gelabert, Victor Resco de Dios, Cristina Vega-García, Luis Torres, Jaime Ribalaygua, and Marcos Rodrigues. "Modeling daily natural-caused ignition probability in the Iberian Peninsula." In Advances in Forest Fire Research 2022, 1214–19. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_184.

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In the European Mediterranean region natural-caused wildfires are a small fraction of total ignitions. Lightning strikes are the most common source of non-human fires, being strongly tied to specific synoptic conditions and patterns associated with atmospheric instability, such as dry thunderstorms. Likewise, lightning-related ignitions often associate with dry fuels and dense vegetation layers. In the case of Iberian Peninsula, the confluence of these factors favors recurrent lightning fires in the eastern Mediterranean mountain ranges and the. However, under appropriate conditions lightning fires can start elsewhere, holding the potential to propagate over vast distances. In this work, we assessed the likelihood of ignition leveraging a large dataset of lightning strikes and historical fires available in Spain. We trained and tested a machine learning model to evaluate the probability of ignition provided that a lightning strikes the ground. Our model was calibrated in the period 2009-2015 using data for mainland Spain plus the Balearic Islands. To build the binary response variable we classified lightning strikes between that triggered a fire event. For each lightning strike we extracted a set of covariates relating fuel moisture conditions, the presence and density of the vegetation layer and the shape of the relief. The final model was subsequently applied to forecast daily probabilities at 1x1 km resolution for the entire Iberian Peninsula. Although the model was originally calibrated in Spain, we extended the predictions to the entire Iberian Peninsula. By doing so we were able to validate in the future our outputs against the Portuguese dataset of recent natural-caused fires (bigger than 1 ha) from 2001 to 2021. Overall, the model attained a great predictive performance with a median AUC of 0.82. Natural-caused ignitions triggered mainly in low dead (dFMC 250) fuel moisture conditions. Lightning strikes with negative polarity seem to trigger fires more frequently when the mean density of discharger was greater than 5. Finally, natural wildfires usually started at higher elevations (above 500 m.a.s.l.).
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Conference papers on the topic "POLARITY DATASET"

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Han, Yi, Mohsen Moghaddam, Meet Tusharbhai Suthar, and Gaurav Nanda. "Aspect-Sentiment-Guided Opinion Summarization for User Need Elicitation From Online Reviews." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-90108.

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Abstract Extracting and analyzing informative user opinion from large-scale online reviews is a key success factor in product design processes. However, user reviews are naturally unstructured, noisy, and verbose. Recent advances in abstractive text summrization provide an unprecedented opportunity to systematically generate summaries of user opinions to facilitate need finding for designers. Yet, two main gaps in the state-of-the-art opinion summarization methods limit their applicability to the product design domain. First is the lack of capabilities to guide the generative process with respect to various product aspects and user sentiments (e.g., polarity, subjectivity), and the second gap is the lack of annotated training datasets for supervised learning. This paper tackles these gaps by (1) devising an efficient and scalable methodology for abstractive opinion summarization from online reviews guided by aspects terms and sentiment polarities, and (2) automatically generating a reusable synthetic training dataset that captures various degrees of granularity and polarity. The methodology contributes a multi-instance pooling model with aspect and sentiment information integrated (MAS), a synthetic data assembled using the results of the MAS model, and a fine-tuned pretrained sequence-to-sequence model “T5” for summary generation. Numerical experiments are conducted on a large dataset scraped from a major e-commerce retail store for sneakers to demonstrate the performance, feasibility, and potentials of the developed methodology. Several directions are provided for future exploration in the area of automated opinion summarization for user-centered product design.
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Mao, Qianren, Jianxin Li, Senzhang Wang, Yuanning Zhang, Hao Peng, Min He, and Lihong Wang. "Aspect-Based Sentiment Classification with Attentive Neural Turing Machines." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/714.

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Aspect-based sentiment classification aims to identify sentiment polarity expressed towards a given opinion target in a sentence. The sentiment polarity of the target is not only highly determined by sentiment semantic context but also correlated with the concerned opinion target. Existing works cannot effectively capture and store the inter-dependence between the opinion target and its context. To solve this issue, we propose a novel model of Attentive Neural Turing Machines (ANTM). Via interactive read-write operations between an external memory storage and a recurrent controller, ANTM can learn the dependable correlation of the opinion target to context and concentrate on crucial sentiment information. Specifically, ANTM separates the information of storage and computation, which extends the capabilities of the controller to learn and store sequential features. The read and write operations enable ANTM to adaptively keep track of the interactive attention history between memory content and controller state. Moreover, we append target entity embeddings into both input and output of the controller in order to augment the integration of target information. We evaluate our model on SemEval2014 dataset which contains reviews of Laptop and Restaurant domains and Twitter review dataset. Experimental results verify that our model achieves state-of-the-art performance on aspect-based sentiment classification.
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Luo, Ling, Xiang Ao, Feiyang Pan, Jin Wang, Tong Zhao, Ningzi Yu, and Qing He. "Beyond Polarity: Interpretable Financial Sentiment Analysis with Hierarchical Query-driven Attention." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/590.

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Sentiment analysis has played a significant role in financial applications in recent years. The informational and emotive aspects of news texts may affect the prices, volatilities, volume of trades, and even potential risks of financial subjects. Previous studies in this field mainly focused on identifying polarity~(e.g. positive or negative). However, as financial decisions broadly require justifications, only plausible polarity cannot provide enough evidence during the decision making processes of humanity. Hence an explainable solution is in urgent demand. In this paper, we present an interpretable neural net framework for financial sentiment analysis. First, we design a hierarchical model to learn the representation of a document from multiple granularities. In addition, we propose a query-driven attention mechanism to satisfy the unique characteristics of financial documents. With the domain specified questions provided by the financial analysts, we can discover different spotlights for queries from different aspects. We conduct extensive experiments on a real-world dataset. The results demonstrate that our framework can learn better representation of the document and unearth meaningful clues on replying different users? preferences. It also outperforms the state-of-the-art methods on sentiment prediction of financial documents.
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Ai, Xinzhi, Xiaoge Li, Feixiong Hu, Shuting Zhi, and Likun Hu. "Multi-Layer Attention Approach for Aspect based Sentiment Analysis." In 9th International Conference on Natural Language Processing (NLP 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101410.

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Based on the aspect-level sentiment analysis is typical of fine-grained emotional classification that assigns sentiment polarity for each of the aspects in a review. For better handle the emotion classification task, this paper put forward a new model which apply Long Short-Term Memory network combine multiple attention with aspect context. Where multiple attention mechanism (i.e., location attention, content attention and class attention) refers to takes the factors of context location, content semantics and class balancing into consideration. Therefore, the proposed model can adaptively integrate location and semantic information between the aspect targets and their contexts into sentimental features, and overcome the model data variance introduced by the imbalanced training dataset. In addition, the aspect context is encoded on both sides of the aspect target, so as to enhance the ability of the model to capture semantic information. The Multi-Attention mechanism (MATT) and Aspect Context (AC) allow our model to perform better when facing reviews with more complicated structures. The result of this experiment indicate that the accuracy of the new model is up to 80.6% and 75.1% for two datasets in SemEval-2014 Task 4 respectively, While the accuracy of the data set on twitter 71.1%, and 81.6% for the Chinese automotive-domain dataset. Compared with some previous models for sentiment analysis, our model shows a higher accuracy.
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John Sino Cruz, Matthew, and Marlene De Leon. "Analysis of citizen's sentiment towards Philippine administration's intervention against COVID-19." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001446.

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The COVID-19 pandemic affected the world. The World Health Organization or WHO issued guidelines the public must follow to prevent the spread of the disease. This includes social distancing, the wearing of facemasks, and regular washing of hands. These guidelines served as the basis for formulating policies by countries affected by the pandemic. In the Philippines, the government implemented different initiatives, following the guidelines of WHO, that aimed to mitigate the effect of the pandemic in the country. Some of the initiatives formulated by the administration include international and domestic travel restrictions, community quarantine, suspension of face-to-face classes and work arrangements, and phased reopening of the Philippine economy to name a few. The initiatives implemented by the government during the surge of COVID-19 disease have resulted in varying reactions from the citizens. The citizens expressed their reactions to these initiatives using different social media platforms such as Twitter and Facebook. The reactions expressed using these social media platforms were used to analyze the sentiment of the citizens towards the initiatives implemented by the government during the pandemic. In this study, a Bidirectional Recurrent Neural Network-Long Short-term memory - Support Vector Machine (BRNN-LSTM-SVM) hybrid sentiment classifier model was used to determine the sentiments of the Philippine public toward the initiatives of the Philippine government to mitigate the effects of the COVID-19 pandemic. The dataset used was collected and extracted from Facebook and Twitter using API and www.exportcomments.com from March 2020 to August 2020. 25% of the dataset was manually annotated by two human annotators. The manually annotated dataset was used to build the COVID-19 context-based sentiment lexicon, which was later used to determine the polarity of each document. Since the dataset contained unstructured and noisy data, preprocessing activities such as conversion to lowercase characters, removal of stopwords, removal of usernames and pure digit texts, and translation to the English language were performed. The preprocessed dataset was vectorized using Glove word embedding and was used to train and test the performance of the proposed model. The performance of the Hybrid BRNN-LSTM-SVM model was compared to BRNN-LSTM and SVM by performing experiments using the preprocessed dataset. The results show that the Hybrid BRNN-LSTM-SVM model, which gained 95% accuracy for the Facebook dataset and 93% accuracy for the Twitter dataset, outperformed the Support Vector Machine (SVM) sentiment model whose accuracy only ranges from 89% to 91% for both datasets. The results indicate that the citizens harbor negative sentiments towards the initiatives of the government in mitigating the effect of the COVID-19 pandemic. The results of the study may be used in reviewing the initiatives imposed during the pandemic to determine the issues which concern the citizens. This may help policymakers formulate guidelines that may address the problems encountered during a pandemic. Further studies may be conducted to analyze the sentiment of the public regarding the implementation of limited face-to-face classes for tertiary education, implementing lesser restrictions, vaccination programs in the country, and other related initiatives that the government continues to implement during the COVID-19 pandemic.
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K. Aryal, Saurav, Howard Prioleau, and Gloria Washington. "Sentiment Classification of Code-Switched Text using Pre-Trained Multilingual Embeddings and Segmentation." In 8th International Conference on Signal, Image Processing and Embedded Systems (SIGEM 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.122013.

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With increasing globalization and immigration, various studies have estimated that about half of the world population is bilingual. Consequently, individuals concurrently use two or more languages or dialects in casual conversational settings. However, most research is natural language processing is focused on monolingual text. To further the work in code-switched sentiment analysis, we propose a multi-step natural language processing algorithm utilizing points of code-switching in mixed text and conduct sentiment analysis around those identified points. The proposed sentiment analysis algorithm uses semantic similarity derived from large pre-trained multilingual models with a handcrafted set of positive and negative words to determine the polarity of code-switched text. The proposed approach outperforms a comparable baseline model by 11.2% for accuracy and 11.64% for F1-score on a Spanish-English dataset. Theoretically, the proposed algorithm can be expanded for sentiment analysis of multiple languages with limited human expertise.
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Davoodi, Laleh, and József Mezei. "A Comparative Study of Machine Learning Models for Sentiment Analysis: Customer Reviews of E-Commerce Platforms." In Digital Restructuring and Human (Re)action. University of Maribor Press, 2022. http://dx.doi.org/10.18690/um.fov.4.2022.13.

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Understanding customers' preferences can be vital for companies to improve customer satisfaction. Reviews of products and services written by customers and published on various online platforms offer tremendous potential to gain important insights about customers' opinions. Sentiment classification with various machine learning models has been of great interest to academia and practice for a while, however, the emergence of language transformer models brings forth new avenues of research. In this article, we compare the performance of traditional machine learning models and recently introduced transformer-based techniques on a dataset of customer reviews published on the Trustpilot platform. We found that transformer-based models outperform traditional models, and one can achieve over 98% accuracy. The best performing model shows the same excellent performance independently of the store considered. We also illustrate why it can be sometimes more reliable to use the sentiment polarity assigned by the machine learning model, rather than a numeric rating that is provided by the customer.
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Oliveira, Miguel V., and Tiago de Melo. "Investigating sets of linguistic features for two sentiment analysis tasks in Brazilian Portuguese web reviews." In Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/webmedia_estendido.2020.13060.

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Identifying subjective sentences and classifying the polarity of subjective sentences are two important tasks in sentiment analysis. Besides being a hot topic, there is still a lack of resources to perform the mentioned sentiment analysis tasks in the Portuguese language, with its syntactic specificities. This paper describes the identified challenges and next steps in an initial study regarding the classification of subjectivity and polarity of sentences with a small set of syntactic features extracted directly from the text. Our approach reached satisfying results in experiments with two classic machine learning models in four datasets consisting of user reviews from different domains.
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Colley, Derek, and Md Asaduzzaman. "Construction and Performance Analysis of a Groomed Polarity Lexicon Derived from Product Review Source Datasets." In 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2021. http://dx.doi.org/10.1109/idaacs53288.2021.9660838.

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Anh, Le Quoc, Vu Duy Thanh, Nguyen Huu Hoang Son, Doan Thi Kim Phuong, Luong Thi Lan Anh, Do Thi Ram, Nguyen Thanh Binh Minh, et al. "Efficient Type and Polarity Classification of Chromosome Images using CNNs: a Primary Evaluation on Multiple Datasets." In 2022 IEEE Ninth International Conference on Communications and Electronics (ICCE). IEEE, 2022. http://dx.doi.org/10.1109/icce55644.2022.9852034.

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Reports on the topic "POLARITY DATASET"

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Farahbod, A. M., and J. F. Cassidy. An overview of seismic attenuation in the Eastern Canadian Arctic and the Hudson Bay Complex, Manitoba, Newfoundland and Labrador, Nunavut, Ontario, and Quebec. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330396.

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In this study we investigated coda-wave attenuation (QC) from the eastern Canadian Arctic in Nunavut and the Hudson Bay complex including portions of northern Manitoba, Ontario, Quebec and Labrador. We used earthquake recordings from 15 broadband and 3 short period seismograph stations of the Canadian National Seismic Network (CNSN) and 29 broadband stations of the POLARIS network across the region. Our dataset is comprised of 637 earthquakes recorded between 1985 and 2021 with magnitudes ranging from 1.3 to 6.1, depths from 0 to 20 km and epicentral distances of 5 to 100 km. This gives a total of 246 high signal-to-noise (S/N) traces (S/N[lesser/equal]5.0) useful for QC calculation (with a maximum ellipse parameter, a2, of 100) across the region. Coda windows were selected to start at tc = 2tS (two times the travel time of the direct S wave), and were filtered at center frequencies of 2, 4, 8, 12 and 16 Hz. Our study reveals a consistent pattern. We find that in the northern section of the study area, the highest Q0 values (e.g., Q0 of 110 and 112) are at station POIN and station RES, respectively, which are located in the older Archean province. The lowest Q0 values that we find (e.g., Q0 of 55 and 61) are at station AKVQ and IVKQ respectively, located in northern Quebec. Smaller Q0 values for stations in the south are explained by the younger age of the rocks and proximity to the main fault systems. An average for all the data results in a Q relationship of QC = 82f1.08 for the frequency band of 2 to 16 Hz for the entire region.
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