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SENTIMENT ANALYSIS USING STRING TOKEN CLASSIFICATION ALGORITHM

Subramaniyaswamy, V and Harshaa, S and Padma Janani, M and Prabhalammbeka, BS SENTIMENT ANALYSIS USING STRING TOKEN CLASSIFICATION ALGORITHM. International Journal of Pure and Applied Mathematics, 2018, vol. 119, n. 12, pp. 13287-13295. [Journal article (Paginated)]

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English abstract

Sentiment analysis is a type of data mining which involves computation of opinions, sentiments and to determine if an information or a piece of text conveys positive, negative or neutral opinion. Public opinion regarding various aspects can be found using sentiment analysis. Clustering and classification are the key techniques in sentiment analysis. Consensus clustering is better than existing clustering algorithms as it provides a stable and efficient final result. However, it has its own drawbacks. Instead of performing consensus clustering and selecting classifiers from the consolidated result, we try to develop a new classification algorithm in our work

Item type:	Journal article (Paginated)
Keywords:	Consensus Clustering, Sentiment Analysis, String Token Classifier, Text Classification
Subjects:	B. Information use and sociology of information B. Information use and sociology of information > BC. Information in society.
Depositing user:	Raster Daster
Date deposited:	02 Aug 2018 07:36
Last modified:	02 Aug 2018 07:36
URI:	http://hdl.handle.net/10760/33269

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