Sentiment Analysis on Tweets

The network of social media involves enormous amount of data being generated everyday by hundreds and thousands of actors. These data can be used for the analysis of collective behavior prediction. Data flooding from social media like Facebook, Twitter, and YouTube presents an opportunity to study collective behavior in a large scale. In today’s world, almost every person updates status, shares pictures, and videos everyday, some even every hour. This has resulted in micro-blogging becoming the popular and most common communication tool of today. The users of micro-blogging Web sites not only share pictures and videos but also share their opinion about any product or issue. Thus, these Web sites provide us with rich sources of data for opinion mining. In this model, our focus is on Twitter, a popular micro-blogging site, for performing the task of opinion mining. The data required for the mining process is collected from Twitter. This data is then analyzed for good and bad tweets, i.e., positive and negative tweets. Based on the number of positive and negative tweets for a particular product, its quality gets determined, and then, the best product gets recommended to the user. Data mining in social media helps us to predict individual user preferences, and the result of which could be used for marketing and advertisement strategies to attract the consumers. In the present world, people tweet in English and regional languages as well. Our model aims to analyze such tweets that have both English words and regional language words pronounced using English alphabets.