Abstract
Opinion characterization is nowadays a potential and intense research focus because of the hasty growth of social media such as blogs and social networking sites, where individuals put in freely their perspectives on different themes. Researches prove that people find it comfortable to opinionate in their mother tongue, be it verbal or written. Given that now almost all social platforms support most of the popular languages, the requirement to mine the sentiments in various dialects is on the rise. However, not all data may be relevant; some may not have any impact on the end result and some may have similar meanings. A preprocessing phase is hence required to help make the dataset concise. In this paper, the authors focus on finding out the polarity of the words input by various users through their reviews exhibited using the South Indian language, Malayalam. Malayalam like the other languages in the Dravidian family exhibits the characteristics of an agglutinative language. The preprocessing process consists of cleaning the data, tokenization, stopword removal, etc. In this paper, authors are focusing on the document-based polarity calculation of the Malayalam reviews. The overall polarity of the corpus is calculated based on the positivity and negativity values of individual documents. It is found that negativity value is higher for the user reviews in our corpus which shows their negative attitude toward the news thread with the classifier accuracy of 89.33%.
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Mathews, D.M., Abraham, S. (2019). Twitter Data Sentiment Analysis on a Malayalam Dataset Using Rule-Based Approach. In: Shetty, N., Patnaik, L., Nagaraj, H., Hamsavath, P., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Advances in Intelligent Systems and Computing, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-6001-5_33
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DOI: https://doi.org/10.1007/978-981-13-6001-5_33
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