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A High End Sentimental Analysis in Social Media Using Hash Tags

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Social media websites can have variety of Linguistic features which provides a variety of useful information’s. Evaluation of usefulness of existing lexical resources as well as features that capture information about the informal and creative language can be used in micro blogging. Social media analysis can be done by Sentiment analysis which refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. We take a supervised approach to the problem, but leverage existing hash tags in the Twitter data for building training data.
In the past few years, there has been a huge growth in the use of micro blogging platforms such as Twitter. Spurred by that growth, companies and media organizations are increasingly seeking ways to mine Twitter for information about what people think and feel about their products and services. Companies such as Twitratr (,tweetfeel(, and Social Mention ( are just a few who advertise Twitter sentiment analysis as one of their services. While there has been a fair amount of research on how sentiments are expressed in genres such as online reviews and news articles, how sentiments are expressed given the informal language and message-length constraints of micro blogging has been much less studied. Features such as automatic part-of-speech tags and resources such as sentiment lexicons have proved useful for sentiment analysis in other domains, but will they also prove useful for sentiment analysis in Twitter? In this paper, we begin to investigate this question.
Another challenge of micro blogging is the incredible breadth of topic that is covered. It is not an exaggeration to say that people tweet about anything and everything. Therefore, to be able to build systems to mine Twitter sentiment about any given topic, we need a method for quickly identifying data that can be used for training. In this paper, we explore one method for building such data: using Twitter hash tags (e.g., #bestfeeling, #epicfail, #news) to identify positive, negative, and neutral tweets to use for training three way sentiment classifiers


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