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dc.contributor.authorDesai, Sanmit Tatoba
dc.description.abstractWith the rising social conflicts in different parts of the world the need to understand the feelings and opinions of the general populous is also growing. Since surveying the whole population is both resource intensive and time consuming we can resort to more a modern approach to this problem. The bloom of social media, especially Mirco-Blogs has extended the horizon of information gathering. With this research, we aim to solve the problem of finding Emotion and Sentiment from the Mirco-Blogging platform Twitter. With 284 million monthly active users and 500 million Tweets generated per day, Twitter contributes to a significant chunk of the vocal population who are not afraid to voice their opinions. What we seek to provide with this work is a fast and accurate way to extract emotions and sentiments from the data Twitter offers. This research is a part of the vision of SMART (Social Media Analysis in Real Time) Barometer which will help us analyze and evaluate text data understanding social conflicts better.
dc.subjectEmotions Analysis, Sentiment Analysis, NLP, Information Gathering, Data Mining, Text Mining, DBpedia
dc.titleSMART Sentiment and Emotion Analysis
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorI. Budak Arpinar
dc.description.committeeI. Budak Arpinar
dc.description.committeeKrzysztof J. Kochut
dc.description.committeeHamid R. Arabnia

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