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

• Autorzy: Muralidhar Rathan , Hulipalled Vishwanath R.

Identyfikatory

DOI

10.2478/fcds-2023-0008

• Języki publikacji: EN

Abstrakty

EN

Social media are a rich source of user generated content where people express their views towards the products and services they encounter. However, sentiment analysis using machine learning models are not easy to implement in a time and cost effective manner due to the requirement of expert human annotators to label the training data. The proposed approach uses a novel method to remove the neutral statements using a combination of lexicon based approach and human effort. This is followed by using a deep active learning model to perform sentiment analysis to reduce annotation efforts. It is compared with the baseline approach representing the neutral tweets also as a part of the data. Considering brands require aspect based ratings towards their products or services, the proposed approach also categorizes predicting ratings of each aspect of mobile device.

Słowa kluczowe

EN

sentiment analysis; opinion mining; Twitter; active learning; deep learning

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2023
• Tom: Vol. 48, No. 2
• Strony: 181--209
• Opis fizyczny: Bibliogr. 59 poz., rys., tab.

Twórcy

autor

Muralidhar Rathan

• Faculty of School of Computing & Information Technology, REVA University, Bangalore, Karnataka, India

autor

Hulipalled Vishwanath R.

• Faculty of School of Computing & Information Technology, REVA University, Bangalore, Karnataka, India

Bibliografia

• [1] Agarwal B., Mittal B., “Machine learning approach for sentiment analysis", in: Prominent feature extraction for sentiment analysis, Springer, 2016, pp. 21-45.
• [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.
• [3] Akhtar N., Zubair N., Kumar A., Ahmad T., “Aspect based sentiment oriented summarization of hotel reviews", Procedia computer science 115 (2017) 563-571.
• [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.
• [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.
• [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.
• [7] Balakrishnan V., Lloyd-Yemoh E., Stemming and lemmatization: “A comparison of retrieval performances", Lecture Notes on Software Engineering 2.
• [8] Balcan F. M. , Broder A., Zhang T., “Margin based active learning", in: International Conference on Computational Learning Theory, Springer, 2007, pp. 35-50.
• [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.
• [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.
• [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.
• [12] Cacciarelli D., Kulahci M., Tyssedal J., “Stream-based active learning with linear models." Knowledge-Based Systems. 254 pp. 109664 (2022).
• [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.
• [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).
• [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).
• [16] Daouadi K., Rebaï 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.
• [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.
• [18] Enginkaya E., Yilmaz 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.
• [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.
• [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.
• [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).
• [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.
• [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.
• [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).
• [25] Jivani A. G., et al., “A comparative study of stemming algorithms", Int. J. Comp. Tech. Appl 2 (6) (2011) 1930-1938.
• [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.
• [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.
• [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.
• [29] Li M., Sethi K. I., “Confidence-based active learning", IEEE transactions on pattern analysis and machine intelligence 28 (8) (2006) 1251-1261.
• [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.
• [31] Liang B., Su H., Gui L., Cambria E, Xu R, “Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks", Knowledge-Based Systems. 235 pp. 107643 (2022), https://www.sciencedirect.com/science/article/pii/S0950705121009059.
• [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.
• [33] Mackoviak R., Lenz P., Ghori O., Diego F., Lange O., Rother C., “Cereals-cost-effective region-based active learning for semantic segmentation", arXiv preprint arXiv:1810.09726.
• [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.
• [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.
• [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.
• [37] Plisson J., Lavrac N., Mladenic D., et al., “A rule based approach to word lemmatization", in: Proceedings of IS, Vol. 3, 2004, pp. 83-86.
• [38] Reidsma D., Akker R., “Exploiting ‘subjective' annotations". Coling 2008: Proceedings Of The Workshop On Human Judgements In Computational Linguistics. pp. 8-16 (2008).
• [39] Roth D., Small K., “Margin-based active learning for structured output spaces", in: European Conference on Machine Learning, Springer, 2006, pp. 413-424.
• [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.
• [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.
• [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.
• [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.
• [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.
• [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.
• [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.
• [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).
• [48] Thrun S., “Exploration in active learning", Handbook of Brain Science and Neural Networks (1995) 381-384.
• [49] Tsimonis G., Dimitriadis S., “Brand strategies in social media", Marketing Intelligence & Planning 32 (2014) 328-344.
• [50] URL - Twitter User Details: https://backlinko.com/twitter-users.
• [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.
• [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.
• [53] Voutilainen A., “Part-of-speech tagging", The Oxford handbook of computational linguistics (2003) 219-232.
• [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.
• [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.
• [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.
• [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.
• [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.
• [59] Zhou Z. H., Li M., “Semi-supervised learning by disagreement", Knowledge and Information Systems 24 (3) (2010) 415-439.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).