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Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "IMDb DATASET"

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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Jnoub, Nour, Fadi Al Machot, and Wolfgang Klas. "A Domain-Independent Classification Model for Sentiment Analysis Using Neural Models." Applied Sciences 10, no. 18 (September 8, 2020): 6221. http://dx.doi.org/10.3390/app10186221.

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Most people nowadays depend on the Web as a primary source of information. Statistical studies show that young people obtain information mainly from Facebook, Twitter, and other social media platforms. By relying on these data, people may risk drawing the incorrect conclusions when reading the news or planning to buy a product. Therefore, systems that can detect and classify sentiments and assist users in finding the correct information on the Web is highly needed in order to prevent Web surfers from being easily deceived. This paper proposes an intensive study regarding domain-independent classification models for sentiment analysis that should be trained only once. The study consists of two phases: the first phase is based on a deep learning model which is training a neural network model once after extracting robust features and saving the model and its parameters. The second phase is based on applying the trained model on a totally new dataset, aiming at correctly classifying reviews as positive or negative. The proposed model is trained on the IMDb dataset and then tested on three different datasets: IMDb dataset, Movie Reviews dataset, and our own dataset collected from Amazon reviews that rate users’ opinions regarding Apple products. The work shows high performance using different evaluation metrics compared to the stat-of-the-art results.

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Kamaru Zaman, Fadhlan Hafizhelmi. "Gender classification using custom convolutional neural networks architecture." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 5758. http://dx.doi.org/10.11591/ijece.v10i6.pp5758-5771.

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Gender classification demonstrates high accuracy in many previous works. However, it does not generalize very well in unconstrained settings and environments. Furthermore, many proposed Convolutional Neural Network (CNN) based solutions vary significantly in their characteristics and architectures, which calls for optimal CNN architecture for this specific task. In this work, a hand-crafted, custom CNN architecture is proposed to distinguish between male and female facial images. This custom CNN requires smaller input image resolutions and significantly fewer trainable parameters than some popular state-of-the-arts such as GoogleNet and AlexNet. It also employs batch normalization layers which results in better computation efficiency. Based on experiments using publicly available datasets such as LFW, CelebA and IMDB-WIKI datasets, the proposed custom CNN delivered the fastest inference time in all tests, where it needs only 0.92ms to classify 1200 images on GPU, 1.79ms on CPU, and 2.51ms on VPU. The custom CNN also delivers performance on-par with state-of-the-arts and even surpassed these methods in CelebA gender classification where it delivered the best result at 96% accuracy. Moreover, in a more challenging cross-dataset inference, custom CNN trained using CelebA dataset gives the best gender classification accuracy for tests on IMDB and WIKI datasets at 97% and 96% accuracy respectively.

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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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Effendi, Fery Ardiansyah, and Yuliant Sibaroni. "Sentiment Classification for Film Reviews by Reducing Additional Introduced Sentiment Bias." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 5 (October 24, 2021): 863–75. http://dx.doi.org/10.29207/resti.v5i5.3400.

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Film business and its individual reviews cannot be separated and film review sites such as IMDb is a credible source of reviews posted in public forums. With IMDb site reviews being unstructured and bias-heavy, classification methods by reducing additional sentiment bias is needed to create a balanced classification with lower polarity bias. Elimination of additional sentiment bias will improve the model as polarity is defined by non-bias method, resulting in models correctly defined which sequences of words is either positive or negative. This research limits the dataset by 50.000 rows of randomly extracted reviews from the IMDb website using dataset preparation methods such as Preprocessing, POS-Tagging, and Word Embeddings. Then preprocessed data is used in classification methods such as ANN, SWN, and SO-Cal. This paper also used bias processing methods such as Hyperparameter Tuning and BPM, with outputs evaluated using Accuracy and PBR metrics. This research yields 77.39 % for ANN, 66.32% for BPM, 75.6% for SO-Cal, and 76.26% for Hybrid classification. Best PBR resulted in two lexicon-based methods on 0.0009 for BPM, and 0.00006 for SO-Cal. More advanced model configuration in ANN can improve the model, and much complex lexicon models will be a future in the research topic.

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Aribowo, Agus Sasmito, Halizah Basiron, and Noor Fazilla Abd Yusof. "Semi-supervised learning for sentiment classification with ensemble multi-classifier approach." International Journal of Advances in Intelligent Informatics 8, no. 3 (November 30, 2022): 349. http://dx.doi.org/10.26555/ijain.v8i3.929.

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Supervised sentiment analysis ideally uses a fully labeled data set for modeling. However, this ideal condition requires a struggle in the label annotation process. Semi-supervised learning (SSL) has emerged as a promising method to avoid time-consuming and expensive data labeling without reducing model performance. However, the research on SSL is still limited and its performance needs to be improved. Thus, this study aims to create a new SSL-Model for sentiment analysis. The Ensemble Classifier SSL model for sentiment classification is introduced. The research went through pre-processing, vectorization, and feature extraction using TF-IDF and n-grams. Support Vector Machine (SVM) or Random Forest for tokenization was used to separate unigram, bigram, and trigram in model generation. Then, the outputs of these models were combined using stacking ensemble approach. Accuracy and F1-score were used for the evaluation. IMDB datasets and US Airlines were used to test the new SSL models. The conclusion is that the sentiment annotation accuracy is highly dependent on the suitability of the dataset with the machine learning algorithm. In IMDB dataset, which consists of two classes, it is better to use SVM. In the US Airlines consisting of three classes, SVM is better at improving the model performance against the baseline, but RF is better at achieving the baseline performance even though it fails to maintain the model performance.

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Shaddeli, Aitak, Farhad Soleimanian Soleimanian Gharehchopogh, Mohammad Masdari, and Vahid Solouk. "An Improved African Vulture Optimization Algorithm for Feature Selection Problems and Its Application of Sentiment Analysis on Movie Reviews." Big Data and Cognitive Computing 6, no. 4 (September 28, 2022): 104. http://dx.doi.org/10.3390/bdcc6040104.

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The African vulture optimization algorithm (AVOA) is inspired by African vultures’ feeding and orienting behaviors. It comprises powerful operators while maintaining the balance of exploration and efficiency in solving optimization problems. To be used in discrete applications, this algorithm needs to be discretized. This paper introduces two versions based on the S-shaped and V-shaped transfer functions of AVOA and BAOVAH. Moreover, the increase in computational complexity is avoided. Disruption operator and Bitwise strategy have also been used to maximize this model’s performance. A multi-strategy version of the AVOA called BAVOA-v1 is presented. In the proposed approach, i.e., BAVOA-v1, different strategies such as IPRS, mutation neighborhood search strategy (MNSS) (balance between exploration and exploitation), multi-parent crossover (increasing exploitation), and Bitwise (increasing diversity and exploration) are used to provide solutions with greater variety and to assure the quality of solutions. The proposed methods are evaluated on 30 UCI datasets with different dimensions. The simulation results showed that the proposed BAOVAH algorithm performed better than other binary meta-heuristic algorithms. So that the proposed BAOVAH algorithm set is the most accurate in 67% of the data set, and 93% of the data set is the best value of the fitness functions. In terms of feature selection, it has shown high performance. Finally, the proposed method in a case study to determine the number of neurons and the activator function to improve deep learning results was used in the sentiment analysis of movie viewers. In this paper, the CNNEM model is designed. The results of experiments on three datasets of sentiment analysis—IMDB, Amazon, and Yelp—show that the BAOVAH algorithm increases the accuracy of the CNNEM network in the IMDB dataset by 6%, the Amazon dataset by 33%, and the Yelp dataset by 30%.

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Zhou, Yancong, Qian Zhang, Dongdong Wang, and Xiaoying Gu. "Text Sentiment Analysis Based on a New Hybrid Network Model." Computational Intelligence and Neuroscience 2022 (December 28, 2022): 1–15. http://dx.doi.org/10.1155/2022/6774320.

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The research of text sentiment analysis based on deep learning is increasingly rich, but the current models still have different degrees of deviation in understanding of semantic information. In order to reduce the loss of semantic information and improve the prediction accuracy as much as possible, the paper creatively combines the doc2vec model with the deep learning model and attention mechanism and proposes a new hybrid sentiment analysis model based on the doc2vec + CNN + BiLSTM + Attention. The new hybrid model effectively exploits the structural features of each part. In the model, the understanding of the overall semantic information of the sentence is enhanced through the paragraph vector pretrained by the doc2vec structure which can effectively reduce the loss of semantic information. The local features of the text are extracted through the CNN structure. The context information interaction is completed through the bidirectional cycle structure of the BiLSTM. The performance is improved by allocating weight and resources to the text information of different importance through the attention mechanism. The new model was built based on Keras framework, and performance comparison experiments and analysis were performed on the IMDB dataset and the DailyDialog dataset. The results have shown that the accuracy of the new model on the two datasets is 91.3% and 93.3%, respectively, and the loss rate is 22.1% and 19.9%, respectively. The accuracy on the IMDB datasets is 1.0% and 0.5% higher than that of the CNN-BiLSTM-Attention model and ATT-MCNN-BGRUM model in the references. Comprehensive comparison has shown the overall performance is improved, and the new model is effective.

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Gore, Mohini, Aishwarya Sheth, Samrudhi Abbad, Paryul Jain, and Prof Pooja Mishra. "IMDB Box Office Prediction Using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2438–42. http://dx.doi.org/10.22214/ijraset.2022.42653.

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Abstract: Movies are a big part of our world! But nobody knows how a movie will perform at the box office. There are some bix budget movies that bomb and there are smaller movies that are smashing successes. This project tries to predict the overall worldwide box office revenue of movies using data such as the movie cast, crew, posters, plot keywords, budget, production companies, release dates, languages, and countries. The dataset on Kaggle contains all these data points that you can use to predict how a movie will fare at the box office. Among many movies that have been released, some generate high profit while the others do not. This paper studies the relationship between movie factors and its revenue and build prediction models. Besides analysis on aggregate data, we also divide data into groups using different methods and compare accuracy across these techniques as well as explore whether clustering techniques could help improve accuracy Keywords: component: regression; predictive analytics; Clustering; Expectation-maximization; K-means; Movies

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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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Дисертації з теми "IMDb DATASET"

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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Частини книг з теми "IMDb DATASET"

Yousuf, Saad Bin, Hasan Sajid, Simon Poon, and Matloob Khushi. "IMDB-Attire: A Novel Dataset for Attire Detection and Localization." In Neural Information Processing, 543–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36711-4_46.

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Feng, Wenying, Daren Zha, Lei Wang, and Xiaobo Guo. "IMDb30: A Multi-relational Knowledge Graph Dataset of IMDb Movies." In Knowledge Science, Engineering and Management, 696–708. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10983-6_53.

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Balci, Salih, Gozde Merve Demirci, Hilmi Demirhan, and Salih Sarp. "Sentiment Analysis Using State of the Art Machine Learning Techniques." In Digital Interaction and Machine Intelligence, 34–42. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11432-8_3.

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AbstractSentiment analysis is one of the essential and challenging tasks in the Artificial Intelligence field due to the complexity of the languages. Models that use rule-based and machine learning-based techniques have become popular. However, existing models have been under-performing in classifying irony, sarcasm, and subjectivity in the text. In this paper, we aim to deploy and evaluate the performances of the State-of-the-Art machine learning sentiment analysis techniques on a public IMDB dataset. The dataset includes many samples of irony and sarcasm. Long-short term memory (LSTM), bag of tricks (BoT), convolutional neural networks (CNN), and transformer-based models are developed and evaluated. In addition, we have examined the effect of hyper-parameters on the accuracy of the models.

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Pachore, A. B., and R. Remesan. "Spatio-Temporal Analysis of Meteorological Drought Using IMD 0.25° Gridded Dataset for Marathwada Region." In Lecture Notes in Civil Engineering, 233–46. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0304-5_18.

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Krishnan, R., J. Sanjay, Chellappan Gnanaseelan, Milind Mujumdar, Ashwini Kulkarni, and Supriyo Chakraborty. "Correction to: Assessment of Climate Change over the Indian Region." In Assessment of Climate Change over the Indian Region, C1. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-4327-2_13.

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In the original version of the book, belated corrections as listed below are incorporated: Chapter 1: The caption of figure 1.5 has been changed as follows: Spatial patterns of change in the June–to-September seasonal precipitation (mm day −1) over the globe in the left-hand column, and over India in the right-hand column. In the top row are plotted the observed changes for the period (1951-2014) relative to (1900-1930) over the globe based on the CRU dataset, and over India based on the IMD dataset. Plots in the middle row are from the IITM-ESM simulations for the historical period, and the plots in the last row are from the IITM-ESM projections following the SSP5-8.5 scenario. The IITM-ESM simulated changes in the historical period (first and middle rows) are plotted as difference for the period (1951-2014) relative to (1850–1900). Changes under the SSP5-8.5 scenario (last row) are plotted as difference between the far-future (2070–2099) relative to (1850–1900). Chapter 2: On page 40, line 5 the word “business-as-usual” has been changed to “twenty-first century under this high emission scenario”. Chapter 4: On page 88, the last sentence “The business as usual scenario will continue to increase atmospheric CO2 and CH4 loading for next several decades.” has been changed to “Without rapid mitigation policies, atmospheric CO2 and CH4 loading will continue to increase for the next several decades.” The erratum chapters and book have been updated with the changes.

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Тези доповідей конференцій з теми "IMDb DATASET"

Tripathi, Sandesh, Ritu Mehrotra, Vidushi Bansal, and Shweta Upadhyay. "Analyzing Sentiment using IMDb Dataset." In 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2020. http://dx.doi.org/10.1109/cicn49253.2020.9242570.

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Sikhi, Yalavarthi, S. Anjali Devi, Sreekar Kumar Jasti, and M. Sitha Ram. "Sentimental Analysis through Speech and text for IMDB Dataset." In 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2022. http://dx.doi.org/10.1109/icssit53264.2022.9716303.

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Mathapati, Savitha, Amulya K. Adur, R. Tanuja, S. H. Manjula, and K. R. Venugopal. "Collaborative Deep Learning Techniques for Sentiment Analysis on IMDb Dataset." In 2018 Tenth International Conference on Advanced Computing (ICoAC). IEEE, 2018. http://dx.doi.org/10.1109/icoac44903.2018.8939068.

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Butler, Martin, and Stefan Robila. "Interface for querying and data mining for the IMDb dataset." In 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT). IEEE, 2016. http://dx.doi.org/10.1109/lisat.2016.7494103.

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Künas, Cristiano Alex, Leandro Perius Heck, and Edson Luiz Padoin. "Implementação de Rede Neural Artificial em Plataforma GPU Aplicada na Análise de Sentimentos em Textos." In Escola Regional de Alto Desempenho da Região Sul. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/eradrs.2020.10751.

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Este artigo apresenta uma proposta de paralelização de uma Rede Neural Artificial em Plataforma GPU para aplicação na análise da polaridade de sentimentos expressado em textos e/ou postagens. Na implementação será utilizado Redes Neurais Recorrentes do tipo Long Short-Term Memory uma vez que Redes Neurais Artificiais podem auxiliar na extração automática de sentimentos ou sensação de sentenças. Com a aplicação da proposta em caso reais a partir do treinamento com o IMDb Review Dataset, que possui 50.000 registros espera-se uma boa precisão nos resultados.

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Попова, Екатерина, Ekaterina Popova, Владимир Спицын, Vladimir Spicyn, Юлия Иванова, and Yuliya Ivanova. "Using artificial neural networks to solve text classification problems." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-1-270-273.

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The article is devoted to neural network text classification algorithms. The relevance of this topic is due to the ever-growing volume of information on the Internet and the need to navigate it. In this paper, in addition to the classification algorithm, a description is also given of the methods of text preprocessing and vectorization, these steps are the starting point for most NLP tasks and make neural network algorithms efficient on small data sets. In the work, a sampling of 50,000 English IMDB movie reviews will be used as a dataset for training and testing the neural network. To solve this problem, an approach based on the use of a convolutional neural network was used. The maximum achieved accuracy for the test sample was 90.16%.

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Bal, Malyaban, and Abhronil Sengupta. "Sequence Learning Using Equilibrium Propagation." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/329.

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Equilibrium Propagation (EP) is a powerful and more bio-plausible alternative to conventional learning frameworks such as backpropagation. The effectiveness of EP stems from the fact that it relies only on local computations and requires solely one kind of computational unit during both of its training phases, thereby enabling greater applicability in domains such as bio-inspired neuromorphic computing. The dynamics of the model in EP is governed by an energy function and the internal states of the model consequently converge to a steady state following the state transition rules defined by the same. However, by definition, EP requires the input to the model (a convergent RNN) to be static in both the phases of training. Thus it is not possible to design a model for sequence classification using EP with an LSTM or GRU like architecture. In this paper, we leverage recent developments in modern hopfield networks to further understand energy based models and develop solutions for complex sequence classification tasks using EP while satisfying its convergence criteria and maintaining its theoretical similarities with recurrent backpropagation. We explore the possibility of integrating modern hopfield networks as an attention mechanism with convergent RNN models used in EP, thereby extending its applicability for the first time on two different sequence classification tasks in natural language processing viz. sentiment analysis (IMDB dataset) and natural language inference (SNLI dataset). Our implementation source code is available at https://github.com/NeuroCompLab-psu/EqProp-SeqLearning.

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Krishna, Muddada Murali, Balaganesh Duraisamy, and Jayavani Vankara. "Hybrid Deep Learning Techniques for Sentiment Analysis on IMDB Datasets." In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2022. http://dx.doi.org/10.1109/icacite53722.2022.9823898.

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La Malfa, Emanuele, Rhiannon Michelmore, Agnieszka M. Zbrzezny, Nicola Paoletti, and Marta Kwiatkowska. "On Guaranteed Optimal Robust Explanations for NLP Models." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/366.

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We build on abduction-based explanations for machine learning and develop a method for computing local explanations for neural network models in natural language processing (NLP). Our explanations comprise a subset of the words of the input text that satisfies two key features: optimality w.r.t. a user-defined cost function, such as the length of explanation, and robustness, in that they ensure prediction invariance for any bounded perturbation in the embedding space of the left-out words. We present two solution algorithms, respectively based on implicit hitting sets and maximum universal subsets, introducing a number of algorithmic improvements to speed up convergence of hard instances. We show how our method can be configured with different perturbation sets in the embedded space and used to detect bias in predictions by enforcing include/exclude constraints on biased terms, as well as to enhance existing heuristic-based NLP explanation frameworks such as Anchors. We evaluate our framework on three widely used sentiment analysis tasks and texts of up to 100 words from SST, Twitter and IMDB datasets, demonstrating the effectiveness of the derived explanations.

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Song, Kaisong, Wei Gao, Shi Feng, Daling Wang, Kam-Fai Wong, and Chengqi Zhang. "Recommendation vs Sentiment Analysis: A Text-Driven Latent Factor Model for Rating Prediction with Cold-Start Awareness." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/382.

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Review rating prediction is an important research topic. The problem was approached from either the perspective of recommender systems (RS) or that of sentiment analysis (SA). Recent SA research using deep neural networks (DNNs) has realized the importance of user and product interaction for better interpreting the sentiment of reviews. However, the complexity of DNN models in terms of the scale of parameters is very high, and the performance is not always satisfying especially when user-product interaction is sparse. In this paper, we propose a simple, extensible RS-based model, called Text-driven Latent Factor Model (TLFM), to capture the semantics of reviews, user preferences and product characteristics by jointly optimizing two components, a user-specific LFM and a product-specific LFM, each of which decomposes text into a specific low-dimension representation. Furthermore, we address the cold-start issue by developing a novel Pairwise Rating Comparison strategy (PRC), which utilizes the difference between ratings on common user/product as supplementary information to calibrate parameter estimation. Experiments conducted on IMDB and Yelp datasets validate the advantage of our approach over state-of-the-art baseline methods.

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