• editor.aipublications@gmail.com
  • Track Your Paper
  • Contact Us
  • ISSN: 2456-2319

International Journal Of Electrical, Electronics And Computers(IJEEC)

Comparative Study on Lexicon-based sentiment analysers over Negative sentiment

Subhasis Sanyal , Mohit Kumar Barai


International Journal of Electrical, Electronics and Computers (IJECC), Vol-6,Issue-6, November - December 2021, Pages 1-13, 10.22161/ijeec.66.1

Download | Downloads : 13 | Total View : 341

Article Info: Received: 25 Sep 2021; Accepted: 03 Nov 2021; Date of Publication: 16 Nov 2021

Share

Sentiment Analysis or Opinion Mining is one of the latest trends of social listening, which is presently reshaping Commercial Organisations. It is a significant task of Natural Language Processing (NLP). The vast availability of product review data within Social media like Twitter, Facebook, and e-commerce site like Amazon, Alibaba. An organisation can get insight into a customer's mind based on a product or what type of opinion the product has generated in the market. Accordingly, an organisation can take some reactive preventive measures. While analysing the above, we have found that negative opinion has a strong effect on customers' minds than the positive one. Also, negative opinions are more viral in terms of diffusion. Our present work is based on a comparison of two available rule-based Sentiment analysers, VADER, and TextBlob on domain-specific product review data from Amazon.co.in. It investigates, which has higher accuracy in terms of classifying negative opinions. Our research has found out that VADER’s negative polarity sentiment classification accuracy is more elevated than TextBlob.

Crowdsourcing, RAKE (Rapid Automatic Keyword Extraction), TextBlob, VADER (Valance Aware Dictionary and sEntiment Reasoner), WOC (Wisdom of Crowd.

[1] Z. Hu, J. Hu, W. Ding and X. Zheng, "Review Sentiment Analysis Based on Deep Learning," 2015 IEEE 12th International Conference on e-Business Engineering, Beijing, 2015, pp. 87-94, doi: 10.1109/ICEBE.2015.24.
[2] Ballestar, M.T.; Cuerdo-Mir, M.; Freire-Rubio, M.T. The Concept of Sustainability on Social Media: A Social Listening Approach. Sustainability 2020, 12, 2122.
[3] Sentiment Analysis and Opinion Mining Bing Liu
Synthesis Lectures on Human Language Technologies 2012 5:1, 1-167
[4] R. K. Bakshi, N. Kaur, R. Kaur and G. Kaur, "Opinion mining and sentiment analysis," 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, 2016, pp. 452-455.
[5] Müller, Sune & Holm, S.R. & Søndergaard, Jens. (2015). Benefits of Cloud Computing: Literature Review in a Maturity Model Perspective. Communications of the Association for Information Systems. 37. 10.17705/1CAIS.03742.
[6] Godbole, Namrata & Srinivasaiah, Manjunath & Skiena, Steven. (2007). Large-Scale Sentiment Analysis for News and Blogs. ICWSM 2007 - International Conference on Weblogs and Social Media.
[7] Tsugawa, Sho & Ohsaki, Hiroyuki. (2015). Negative Messages Spread Rapidly and Widely on Social Media. 151-160. 10.1145/2817946.2817962.
[8] Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad Is Stronger Than Good. Review of General Psychology, 5(4), 323-370. https://doi.org/10.1037/1089-2680.5.4.323
[9] Rozin, Paul & Royzman, Edward. (2001). Negativity Bias, Negativity Dominance, and Contagion. Personality and Social Psychology Review. 5. 10.1207/S15327957PSPR0504_2.
[10] Mudambi, Susan & Schuff, David. (2010). What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com.. MIS Quarterly. 34. 185-200. 10.2307/20721420.
[11] Rose, Stuart & Engel, Dave & Cramer, Nick & Cowley, Wendy. (2010). Automatic Keyword Extraction from Individual Documents. 10.1002/9780470689646.ch1.
[12] Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
[13] James Surowiecki. 2005. The Wisdom of Crowds. Anchor.
[14] Steven Loria,'textblob DocumentationRelease 0.16.0'
[15] Taboada, Maite & Brooke, Julian & Tofiloski, Milan & Voll, Kimberly & Stede, Manfred. (2011). Lexicon-Based Methods for Sentiment Analysis. Computational Linguistics. 37. 267-307. 10.1162/COLI_a_00049.
[16] Tong, R.M. (2001) An Operational System for Detecting and Tracking Opinions in On-Line Discussion. Proceedings of SIGIR Workshop on Operational Text Classification.
[17] Jurek, A., Mulvenna, M.D. & Bi, Y. Improved lexicon-based sentiment analysis for social media analytics. Secur Inform 4, 9 (2015).
[18] Bouazizi, Mondher & Ohtsuki, Tomoaki. (2017). A Pattern-Based Approach for Multiclass Sentiment Analysis in Twitter. IEEE Access. PP. 1-1. 10.1109/ACCESS.2017.2740982.
[19] Bonta, Venkateswarlu & Kumaresh, Nandhini & Janardhan, N.. (2019). A Comprehensive Study on Lexicon Based Approaches for Sentiment Analysis. 1-6.
[20] Boiy, Erik & Moens, Marie-Francine. (2009). A Machine Learning Approach to Sentiment Analysis in Multilingual Web Texts. Inf. Retr.. 12. 526-558. 10.1007/s10791-008-9070-z.
[21] Polanyi L., Zaenen A. (2006) Contextual Valence Shifters. In: Shanahan J.G., Qu Y., Wiebe J. (eds) Computing Attitude and Affect in Text: Theory and Applications. The Information Retrieval Series, vol 20. Springer, Dordrecht. https://doi.org/10.1007/1-4020-4102-0_1
[22] H. Parveen and S. Pandey, "Sentiment analysis on Twitter Data-set using Naive Bayes algorithm," 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Bangalore, 2016, pp. 416-419, doi: 10.1109/ICATCCT.2016.7912034.
[23] Kang, Hanhoon & Yoo, Seong & Han, Dongil. (2012). Senti-lexicon and improved Na??ve Bayes algorithms for sentiment analysis of restaurant reviews. Expert Systems with Applications. 39. 6000-6010. 10.1016/j.eswa.2011.11.107.
[24] K. Mouthami, K. N. Devi and V. M. Bhaskaran, "Sentiment analysis and classification based on textual reviews," 2013 International Conference on Information Communication and Embedded Systems (ICICES), Chennai, 2013, pp. 271-276, doi: 10.1109/ICICES.2013.6508366.
[25] Bollegala, T. Mu and J. Y. Goulermas, "Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings," in IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 2, pp. 398-410, 1 Feb. 2016, doi: 10.1109/TKDE.2015.2475761.
[26] Parvati Kadli and Vidyavathi B M.. Cross Domain Sentiment Classification Techniques: A Review. International Journal of Computer Applications 181(37):13-20, January 2019.
[27] Recognising Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis Theresa Wilson author Janyce Wiebe author Paul Hoffmann author 2009 text Computational Linguistics continuing periodical academic journal wilson-etal-2009-articles 10.1162/coli.08-012-R1-06-90
[28] Mannes, A. E., Larrick, R. P., & Soll, J. B. (2012). The social psychology of the Wisdom of crowds. In J. I. Krueger (Ed.), Frontiers of social psychology. Social judgment and decision making (p. 227–242). Psychology Press.
[29] Durward, D., Blohm, I. & Leimeister, J.M. Crowd Work. Bus Inf Syst Eng 58, 281–286 (2016). https://doi.org/10.1007/s12599-016-0438-0
[30] Herzog, S. M., & Hertwig, R. (2014). Think twice and then: Combining or choosing in dialectical bootstrapping? Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(1), 218–232. https://doi.org/10.1037/a0034054
[31] Goldstein, Daniel & McAfee, Randolph & Suri, Siddharth. (2014). The Wisdom of smaller, smarter crowds. EC 2014 - Proceedings of the 15th ACM Conference on Economics and Computation. 10.1145/2600057.2602886.
[32] arXiv:1301.3781 [cs.CL]
[33] Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124-1131.