Sentiment analysis for social media

Abstract

Sentiment analysis, the automated extraction of expressions of positive or negative attitudes from text has received considerable attention from researchers during the past decade. In addition, the popularity of internet users has been growing fast parallel to emerging technologies; that actively use online review sites, social networks and personal blogs to express their opinions. They harbor positive and negative attitudes about people, organizations, places, events, and ideas. The tools provided by natural language processing and machine learning along with other approaches to work with large volumes of text, makes it possible to begin extracting sentiments from social media. In this paper we discuss some of the challenges in sentiment extraction, some of the approaches that have been taken to address these challenges and our approach that analyses sentiments from Twitter social media which gives the output beyond just the polarity but use those polarities in product profiling, trend analysis and forecasting. Promising results has shown that the approach can be further developed to cater business environment needs through sentiment analysis in social media.