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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/4028

Title: Opinion Mining Restaurant Reviews on Demand
Authors: Perera, I.K.C.U.
Issue Date: 2017
Abstract: Since expansion of social media and internet are driving to a whole another level, most of the users critically review anything on the internet specially foods and services in restaurants to showcase their humble opinion. These opinions are very valuable in decision making process. Analyzing and extracting the actual opinion throughout these reviews manually is practically difficult since there are large numbers of reviews available in the various aspects. So, an automated methodology is needed to solve this problem. Opinion mining or sentiment analysis is such methodology to analysis these reviews and classify topics as positive, negative and neutral. There are three different levels of opinion mining; Document based, Sentence based and Aspect based. Document and Sentence based opinion mining focus on overall polarity of document and sentence respectively and do not describe the important aspects of each opinion which is more accurate. Hence Aspect based opining mining is the trending topic and this thesis is specifically focused on it on reviews in the domain of restaurants. This empirical work is done for restaurant reviews. Aspect extraction and orientation detection has been used to find the output of this research. This proposed system satisfies 70% of the research objective.
URI: http://hdl.handle.net/123456789/4028
Appears in Collections:Master of Computer Science - 2017

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