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AMRITA-CEN@SAIL2015: Sentiment analysis in Indian languages

Publication Type : Journal Article

Thematic Areas : Center for Computational Engineering and Networking (CEN)

Publisher : Springer Verlag

Source : Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, Volume 9468, MIKE 2015; Hyderabad; India;, p.703-710 (2015)

Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955308904&partnerID=40&md5=c375439cecf4fcec1f74b8898a9580b2

ISBN : 9783319268316

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Computer Science, Electronics and Communication

Year : 2015

Abstract : The contemporary work is done as slice of the shared task in Sentiment Analysis in Indian Languages (SAIL 2015), constrained variety. Social media allows people to create and share or exchange opinions based on many perspectives such as product reviews, movie reviews and also share their thoughts through personal blogs and many more platforms. The data available in the internet is huge and is also increasing exponentially. Due to social media, the momentousness of categorizing these data has also increased and it is very difficult to categorize such huge data manually. Hence, an improvised machine learning algorithm is necessary for wrenching out the information. This paper deals with finding the sentiment of the tweets for Indian languages. These sentiments are classified using various features which are extracted using words and binary features, etc. In this paper, a supervised algorithm is used for classifying the tweets into positive, negative and neutral labels using Naive Bayes classifier. © Springer International Publishing Switzerland 2015.

Cite this Research Publication : S. Se, Vinayakumar, R., M. Kumar, A., and Soman, K. P., “AMRITA-CEN@SAIL2015: Sentiment analysis in Indian languages”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9468, pp. 703-710, 2015.

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