A comparative review of the challenges encountered in sentiment analysis of Indian regional language tweets vs English language tweets
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2018-04-20 https://doi.org/10.14419/ijet.v7i2.21.12394 -
Sentiment analysis, Indian regional language tweets, challenges in sentiment analysis, twitter sentiment analysis of English tweets. -
Abstract
With the developed use of online medium these days for sharing views, sentiments and opinions about products, services, organization and people, micro blogging and social networking sites are acquiring a huge popularity. One of the biggest social media sites namely Twitter is used by several people to share their life events, views and opinion about different areas and concepts. Sentiment analysis is the computational research of reviews, opinions, attitudes, views and peoples’ emotions about different products, services, firms and topics through categorizing them as negative and positive emotions. Sentiment analysis of tweets is a challenging task. This paper makes a critical review on the comparison of the challenges associated with sentiment analysis of Tweets in English Language versus Indian Regional Languages. Five Indian languages namely Tamil, Malayalam, Telugu, Hindi and Bengali have been considered in this research and several challenges associated with the analysis of Twitter sentiments in those languages have been identified and conceptualized in the form of a framework in this research through systematic review.
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- [1] Venugopalan M & Gupta D, “Exploring sentiment analysis on twitter dataâ€, Proceedings of eighth International Conference on Contemporary Computing, (2015), pp.241-243.[2] Chalothom T & Ellman J, “Simple approaches of sentiment analysis via ensemble learningâ€, Information science and applications, (2015), pp.631-639.[3] Narr S, Hulfenhaus M & Albayrak S, “Language-independent twitter sentiment analysisâ€, Knowledge discovery and machine learning (KDML), (2012), pp.12-14.[4] Riloff E, Qadir A, Surve P, De Silva L, Gilbert N & Huang R, “Sarcasm as contrast between a positive sentiment and negative situationâ€, Proceedings of Conference on Empirical Methods in Natural Language Processing, (2013), pp.704-714.[5] Kaur J, “A Review Paper on Twitter Sentiment Analysis Techniquesâ€, International Journal for Research in Applied Science & Engineering Technology, Vol.4, No.10, (2016), pp.61-69.[6] Remus R, “Modeling and Representing Negation in Data-driven Machine Learning-based Sentiment Analysisâ€, ESSEM@ AI* IA , (2013), pp.22-33.[7] Irvine A & Callison-Burch C, “Combining bilingual and comparable corpora for low resource machine translationâ€, Proceedings of the eighth workshop on statistical machine translation, (2013), pp. 262–270.[8] Severyn A & Moschitti A, “Twitter sentiment analysis with deep convolutional neural networksâ€, SIGIR, (2015), pp. 959–962.[9] Xiang B & Zhou L, “Improving twitter sentiment analysis with topic-based mixture modeling and semi-supervised trainingâ€, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Vol.2, (2014), pp.434-439.
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How to Cite
Jacob Soman, S., Swaminathan, P., Anandan, R., & Kalaivani, K. (2018). A comparative review of the challenges encountered in sentiment analysis of Indian regional language tweets vs English language tweets. International Journal of Engineering & Technology, 7(2.21), 319-322. https://doi.org/10.14419/ijet.v7i2.21.12394Received date: 2018-05-03
Accepted date: 2018-05-03
Published date: 2018-04-20