Hybrid approach to SVM algorithm for sentiment analysis of tweets.

The community#x2019;s views and inputs have always been the main and most beneficial source for varied range of enterprises. With more widespread community media, it provides a spectacular study and assessment of many fields in which companies used to have faith in peculiar, exhausting and inaccurate ways. This form of analysis is subclass of #x2019;sentence analysis#x2019; area. Sentiment analysis is a broad term that refers to the process of effectively classifying user-generated content into specific polarities. To perform sentiment identification and analysis, a variety of tools and techniques are available, includes supervised techniques for machine-learning that classify the target group after training in data. Hybrid instruments are a blend of machine learning and lexicon-based algorithms, which classify according to annotated dictionary. We employed the SVM with Weka for analyzing sentiments in this paper. Two pre-categorized datasets of tweets are utilized. The performance of SVM is analyzed with the help of analytical metrics. [ABSTRACT FROM AUTHOR]