Sentiment Analysis on Twitter Data using SVM Classifier

Journal

International Journal of Digital Applications and Contemporary Research (IJDACR)

ISSN

2319-4863

Volume / Issue

Vol.8 · Issue 2

Published

September 2019

Access

Open Access

Licence

CC BY-NC-SA 4.0

Kanak Jagwani Ishi Raghuvanshi Janhvi Sharma

Analyzing the large volumes of data generated in social networks on public opinion about different topics can result in valuable discoveries. These activities are expensive to perform manually, they require many human resources and time. Sentiment analysis systems and data mining algorithms have proved to be very useful in order to obtain a general perception of the topics of interest and the opinion on them. In this paper we propose to analyze a set of data using a sentiment classifier to label publications made by users of social networks in conjunction with clustering algorithms to be able to detect which are the topics on which opinions are expressed. We propose to use a base of 2000 reviews of films labeled as positive and negative and then train support vector machine (SVM) classifier of sentiments. We performed our experiments using one thousand tweets. Experimental evaluations show that our proposed technique is more efficient and has higher accuracy compared to previously proposed methods.

Machine Learning Sentiment Analysis SVM Web 2.0

Kanak Jagwani, Ishi Raghuvanshi, Janhvi Sharma (2019). Sentiment Analysis on Twitter Data using SVM Classifier. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.8, Issue 2. ISSN: 2319-4863.

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