Sentiment Analysis Framework using Deep Active Learning for Smartphone Aspect Based Rating Prediction

[1] Agarwal B, Mittal B, “Machine learning approach for sentiment analysis”, in: Prominent feature extraction for sentiment analysis, Springer, 2016, pp. 21–45. Search in Google Scholar

[2] Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R.J., “Sentiment analysis of twitter data”, in: Proceedings of the workshop on language in social media (LSM 2011), 2011, pp. 30–38. Search in Google Scholar

[3] Akhtar N, Zubair N, Kumar A, Ahmad T, “Aspect based sentiment oriented summarization of hotel reviews”, Procedia computer science 115 (2017) 563–571. Search in Google Scholar

[4] Ansari M.Z., Aziz M, Siddiqui M, Mehra H, Singh K., “Analysis of political sentiment orientations on twitter”, Procedia Computer Science 167 (2020) 1821–1828. Search in Google Scholar

[5] Arora S, Nyberg E, Rose C, “Estimating annotation cost for active learning in a multi-annotator environment”, in: Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing, 2009, pp. 18–26. Search in Google Scholar

[6] Arruda G, Roman N, Monteiro A, “Analysing Bias in Political News.”, JUCS - Journal of Universal Computer Science(2020), 173–199. https://doi.org/10.3897/jucs.2020.011 Search in Google Scholar

[7] Balakrishnan V, Lloyd-Yemoh E, Stemming and lemmatization: “A comparison of retrieval performances”, Lecture Notes on Software Engineering 2. Search in Google Scholar

[8] Balcan F.M., Broder A, Zhang T, “Margin based active learning”, in: International Conference on Computational Learning Theory, Springer, 2007, pp. 35–50. Search in Google Scholar

[9] Begum S, Bera S, Chakraborty D, Sarkar R, “Breast cancer detection using feature selection and active learning”, in: Computer, communication and electrical technology: proceedings of the international conference on advancement of computer communication and electrical technology (ACCET 2016), West Bengal, India, 21-22 October 2016, The British Institute of Radiology., 2017, pp. 43–8. Search in Google Scholar

[10] Bianchi F.M., Maiorino E, Kampffmeyer M.C., Rizzi A, Jenssen R, “Recurrent neural network architectures”, in: Recurrent Neural Networks for Short-Term Load Forecasting, Springer, 2017, pp. 23–29. Search in Google Scholar

[11] Bose R, Dey R.K., Roy S, Sarddar D, “Analyzing political sentiment using twitter data”, in: Information and Communication Technology for Intelligent Systems, Springer, 2019, pp. 427–436. Search in Google Scholar

[12] Cacciarelli D., Kulahci M., Tyssedal J, “Stream-based active learning with linear models.” Knowledge-Based Systems. 254 pp. 109664 (2022) Search in Google Scholar

[13] Carse J, McKenna S, “Active learning for patch-based digital pathology using convolutional neural networks to reduce annotation costs”, in: European Congress on Digital Pathology, Springer, 2019, pp. 20–27. Search in Google Scholar

[14] Aydin C., Güngör T, “Combination of recursive and recurrent neural networks for aspect-based sentiment analysis using inter-aspect relations.” IEEE Access. 8 pp. 77820-77832 (2020) Search in Google Scholar

[15] Culnan M.J., McHugh P.J., Zubillaga J.I., “How large us companies can use twitter and other social media to gain business value.”, MIS Quarterly Executive 9 (4). Search in Google Scholar

[16] Daouadi K, Reba¨ı R, Amous I, “Real-Time Bot Detection from Twitter Using the Twitterbot+ Framework”, JUCS - Journal of Universal Computer Science(2020), 496–507. https://doi.org/10.3897/jucs.2020.026 Search in Google Scholar

[17] Do H.H., Prasad P, Maag A, Alsadoon A,“Deep learning for aspect-based sentiment analysis”: a comparative review, Expert Systems with Applications 118 (2019) 272–299. Search in Google Scholar

[18] Enginkaya E, Yılmaz H, “What drives consumers to interact with brands through social media? a motivation scale development study”, Procedia-Social and Behavioral Sciences 148 (2014) 219–226. Search in Google Scholar

[19] Fan M, Billings A, Zhu X, Yu P, “Big data analysis of english national team fans during the 2018 fifa world cup”, Communication & Sport 8 (3) (2020) 317–345. Search in Google Scholar

[20] Freund Y, Seung S.H., Shamir E, Tishby N, “Selective sampling using the query by committee algorithm”, Machine learning 28 (2) (1997) 133–168. Search in Google Scholar

[21] Friguet C, Dambreville R, Kijak E, Laroze M, Lefèvre S, “Data annotation with active learning: application to environmental surveys”, JDS Journées De Statistique De La Société Française De Statistique (SFdS). pp. 1-6 (2020) Search in Google Scholar

[22] Gundecha P, Liu H, “Mining social media: a brief introduction”, Institute for Operations Research and the Management Sciences (INFORMS), TutORials in Operations Research. (2014) 1–17. Search in Google Scholar

[23] Gupta V, Singh V.K., Mukhija P, Ghose U, “Aspect-based sentiment analysis of mobile reviews”, Journal of Intelligent & Fuzzy Systems 36 (5) (2019) 4721–4730. Search in Google Scholar

[24] Hew K, Hu X, Qiao C, Tang Y, “What predicts student satisfaction with MOOCs; A gradient boosting trees supervised machine learning and sentiment analysis approach”, Computers & Education, 145 pp. 103724 (2020) Search in Google Scholar

[25] Jivani A.G., et al., “A comparative study of stemming algorithms”, Int. J. Comp. Tech. Appl 2 (6) (2011) 1930–1938. Search in Google Scholar

[26] Ibrahim N.F., Wang X, “A text analytics approach for online retailing service improvement: Evidence from twitter”, Decision Support Systems 121 (2019) 37–50. Search in Google Scholar

[27] Ibrahim N.F., Wang X, Bourne H, “Exploring the effect of user engagement in online brand communities: Evidence from twitter”, Computers in Human Behavior 72 (2017) 321–338. Search in Google Scholar

[28] Lango M, “Tackling the Problem of Class Imbalance in Multi-class Sentiment Classification: An Experimental Study”, Foundations Of Computing And Decision Sciences. 151-178 (2019), https://doi.org/10.2478/fcds-2019-0009 Search in Google Scholar

[29] Li M, Sethi K.I., “Confidence-based active learning”, IEEE transactions on pattern analysis and machine intelligence 28 (8) (2006) 1251–1261. Search in Google Scholar

[30] Li Q, Shah S, “Learning stock market sentiment lexicon and sentiment-oriented word vector from stocktwits”, in: Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), 2017, pp. 301–310. Search in Google Scholar

[31] Liang B, Su H, Gui L, Cambria E, Xu R, “ Aspect-based sentiment analysis via a effective knowledge enhanced graph convolutional networks”, Knowledge-Based Systems. 235 pp. 107643 (2022), https://www.sciencedirect.com/science/article/pii/S0950705121009059 Search in Google Scholar

[32] Luan Y, Lin S, “Research on text classification based on cnn and lstm”, in: 2019 IEEE international conference on artificial intelligence and computer applications (ICAICA), IEEE, 2019, pp. 352–355. Search in Google Scholar

[33] Mackoviak R, Lenz P, Ghori O, Diego F, Lange O, Rother C, “Cereals-costeffective region-based active learning for semantic segmentation”, arXiv preprint arXiv:1810.09726. Search in Google Scholar

[34] Oliveira N, Cortez P, Areal N, “Stock market sentiment lexicon acquisition using microblogging data and statistical measures”, Decision Support Systems 85 (2016) 62–73. Search in Google Scholar

[35] Olsson F, “A literature survey of active machine learning in the context of natural language processing”, SICS Technical Report T2009:06 ISSN: 1100-3154. Search in Google Scholar

[36] Pimprikar R, Ramachadran S, Senthilkumar K, “Use of machine learning algorithms and twitter sentiment analysis for stock market prediction”, Int J Pure Appl Math 115 (6) (2017) 521–526. Search in Google Scholar

[37] Plisson J, Lavrac N, Mladenic D, et al., “A rule based approach to word lemma-tization”, in: Proceedings of IS, Vol. 3, 2004, pp. 83–86. Search in Google Scholar

[38] Reidsma, D, Akker, R, “Exploiting ‘subjective’annotations”. Coling 2008: Proceedings Of The Workshop On Human Judgements In Computational Linguistics. pp. 8-16 (2008) Search in Google Scholar

[39] Roth D, Small K, “Margin-based active learning for structured output spaces”, in: European Conference on Machine Learning, Springer, 2006, pp. 413–424. Search in Google Scholar

[40] Schumaker R.P., Jarmoszko A.T., Labedz Jr C.S., “Predicting wins and spread in the premier league using a sentiment analysis of twitter”, Decision Support Systems 88 (2016) 76–84. Search in Google Scholar

[41] Schumaker R.P., Labedz Jr C.S., Jarmoszko A.T., Brown L.L, “Prediction from regional angst–a study of nfl sentiment in twitter using technical stock market charting”, Decision Support Systems 98 (2017) 80–88. Search in Google Scholar

[42] Schumann R, Rehbein I, “Active learning via membership query synthesis for semi-supervised sentence classification”, in: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), Association for Computational Linguistics, Hong Kong, China, 2019, pp. 472–481. Search in Google Scholar

[43] Simard P, Chickering D, Lakshmiratan A, Charles D, Bottou L, Suarez C.G.J, Grangier D, Amershi S, Verwey J, Suh J, “Ice: enabling non-experts to build models interactively for large-scale lopsided problems”, arXiv preprint arXiv:1409.4814. Search in Google Scholar

[44] Slimi H, Bounhas I, Slimani Y, “Adapting Pre-trained Language Models to Rumor Detection on Twitter”, JUCS - Journal of Universal Computer Science(2021), 1128–1148. https://doi.org/10.3897/jucs.65918 Search in Google Scholar

[45] Smagulova K, James A.P., “A survey on lstm memristive neural network architectures and applications”, The European Physical Journal Special Topics 228 (10) (2019) 2313–2324. Search in Google Scholar

[46] Song J, Wang H, Gao Y, An B, “Active learning with confidence-based answers for crowdsourcing labeling tasks”, Knowledge-Based Systems 159 (2018) 244–258. Search in Google Scholar

[47] Sun K, Zhang R, Mensah S, Mao Y, Liu X, “Aspect-level sentiment analysis via convolution over dependency tree”, Proceedings Of The 2019 Conference On Empirical Methods In Natural Language Processing And The 9th International Joint Conference On Natural Language Processing (EMNLP-IJCNLP). pp. 5679-5688 (2019) Search in Google Scholar

[48] Thrun S, “Exploration in active learning”, Handbook of Brain Science and Neural Networks (1995) 381–384. Search in Google Scholar

[49] Tsimonis G, Dimitriadis S, “Brand strategies in social media”, Marketing Intelligence & Planning 32 (2014) 328–344. Search in Google Scholar

[50] URL - Twitter User Details: https://backlinko.com/twitter-users Search in Google Scholar

[51] Vanaja S, Belwal M, “Aspect-level sentiment analysis on e-commerce data”, in: 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE, 2018, pp. 1275–1279. Search in Google Scholar

[52] Von Helversen B, Abramczuk K, Kopeć W, Nielek R, “Influence of consumer reviews on online purchasing decisions in older and younger adults”, Decision Support Systems 113 (2018) 1–10. Search in Google Scholar

[53] Voutilainen A, “Part-of-speech tagging”, The Oxford handbook of computational linguistics (2003) 219–232. Search in Google Scholar

[54] Wang H, Jin Y, Doherty J, “Committee-based active learning for surrogate-assisted particle swarm optimization of expensive problems”, IEEE transactions on cybernetics 47 (9) (2017) 2664–2677. Search in Google Scholar

[55] Webster J.J., Kit C., “Tokenization as the initial phase in nlp”, in: COLING 1992 Volume 4: The 15th International Conference on Computational Linguistics, 1992. Search in Google Scholar

[56] Wu T, Weld D.S., Heer J, “Local decision pitfalls in interactive machine learning: An investigation into feature selection in sentiment analysis”, ACM Transactions on Computer-Human Interaction (TOCHI) 26 (4) (2019) 1–27. Search in Google Scholar

[57] Yan Y, Rosales R, Fung G, Schmidt M, Hermosillo G, Bogoni L, Moy L, Dy J, “Modeling annotator expertise: Learning when everybody knows a bit of something”, in: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, 2010, pp. 932–939. Search in Google Scholar

[58] Yan Y, Rosales R, Fung G, Subramanian R, Dy J, “Learning from multiple annotators with varying expertise”, Machine learning 95 (3) (2014) 291–327. Search in Google Scholar

[59] Zhou Z.H., Li M, “Semi-supervised learning by disagreement”, Knowledge and Information Systems 24 (3) (2010) 415–439. Search in Google Scholar