Mining Indian Tweets to Understand Food Price Rise Crisis

Abstract

The work presented in this thesis takes place in the field of text mining and aims more particularly at finding the sentiments of a text. Sentiment analysis is the ongoing field of research in text mining field. The most important achievement of the web, which enables a large number of web users to discuss different issues, is providing immense help to a number of organizations. The help is provided in the form of product reviews, movie reviews and stock market predictions etc. In this paper, the possible role of sentiment analysis in different domains has been projected. The objective of this work is to comparatively analyze the number of techniques used in different domains and various challenges embedded in sentiment analysis. In sentiment analysis, we have to extract the sentiments associated with particular text and to calculate the polarity based on the context of data. Finally, this research provides a hybrid approach for mining Indian tweets for understanding food price crisis. This approach deals with extracting features for creating lexicon and calculating polarity. An enhanced scheme for sentiment analysis of social networking sites can help to understand the food price rise crisis as food price has direct impact on the purchasing power of a large part of Indian population.