Sentiment Analysis of Tweets using SVM

Community's view and feedback have always proved to be the most essential and valuable resource for companies and organizations. With social media being the emerging trend among everyone, it paves way for unprecedented analysis and evaluation of various aspects for which organizations had to rely on unconventional, time consuming and error prone methods earlier. This technique of analysis directly falls under the domain of "sentiment analysis". Sentiment analysis encompasses the vast field of effective classification of user generated text under defined polarities. There are several tools and algorithms available to perform sentiment detection and analysis including supervised machine learning algorithms that perform classification on the target corpus, after getting trained with training data. Lexical techniques which performs classification on the basis of dictionary based annotated corpus and Hybrid tools which are combination of machine learning and lexicon based algorithms. In this paper we have used Support Vector Machine (SVM) for sentiment analysis in Weka. SVM is one of the widely used supervised machine learning algorithms for textual polarity detection. To analyze the performance of SVM, two pre classified datasets of tweets are used and for comparative analysis, three measures are used: Precision, Recall and F-Measure. Results are shown in the form of tables and graphs.