Precise tweet classification and sentiment analysis

The rise of social media in couple of years has changed the general perspective of networking, socialization, and personalization. Use of data from social networks for different purposes, such as election prediction, sentimental analysis, marketing, communication, business, and education, is increasing day by day. Precise extraction of valuable information from short text messages posted on social media (Twitter) is a collaborative task. In this paper, we analyze tweets to classify data and sentiments from Twitter more precisely. The information from tweets are extracted using keyword based knowledge extraction. Moreover, the extracted knowledge is further enhanced using domain specific seed based enrichment technique. The proposed methodology facilitates the extraction of keywords, entities, synonyms, and parts of speech from tweets which are then used for tweets classification and sentimental analysis. The proposed system is tested on a collection of 40,000 tweets. The proposed methodology has performed better than the existing system in terms of tweets classification and sentiment analysis. By applying the Knowledge Enhancer and Synonym Binder module on the extracted information we have achieved increase in information gain in a range of 0.1% to 55%. The increase in information gain has enabled our proposed system to better summarize the twitter data for user sentiments regarding a keyword from a particular category.