Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/12410
Authors: Agarwal, Pracheer
Issue Date: 2011
Abstract: "What other people think" has always been an important piece of information for most of us during the decision-making process. Long before awareness of the World Wide Web became widespread, many of us asked our friends for suggestions. But the Internet and the Web have now made it possible to find out about the opinions and experiences of those in the vast pool of people that are neither our personal acquaintances nor well-known professional critics — that is, people we have never heard of And conversely, more and more people are making their opinions available to strangers via the Internet. In recent years, there has been a rapid growth of web-content, especially on-line discussion groups, review sites and blogs. These are highly personal and typically express opinions. To organize this information, identification of sentiment polarity is very useful. Sentiment analysis or opinion mining is a branch of natural language processing, computational linguistics and text mining. The main task in Sentiment Analysis is to find out the mood of writer or speaker with respect to some topic. Most of the previous attempts to extract sentiment from sentence focused on the use of machine learning methods ignoring the importance of language analysis. We present an approach to find the hidden sentiment expressed in text at sentence level in the presence of conjunctions. Different approaches have been used to find sentiment, but none of those ever considered the conjunctions used in the sentence. We have formed a rule set for different conjunctions to join the sentiments expressed in different phrases of the sentence. Several experiments with datasets have been conducted. The experimental results shows significant performance gain over existing approaches.
Other Identifiers: M.Tech
Research Supervisor/ Guide: Joshi, R. C.
metadata.dc.type: M.Tech Dessertation
Appears in Collections:MASTERS' DISSERTATIONS (E & C)

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