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    • Thesis & Report, BSc (Computer Science and Engineering)
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    •   BracU IR
    • School of Engineering and Computer Science (SECS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    An effective machine learning approach for sentiment analysis of restaurant reviews

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    13101248_CSE.pdf (1.067Mb)
    Date
    2016-08
    Publisher
    BARC University
    Author
    Karim, Rabita
    Metadata
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    URI
    http://hdl.handle.net/10361/9023
    Abstract
    Sentiment analysis or opinion analysis is creating a vast area of research in this modern era of social media. Various blogs and Social Medias (Facebook, twitter, Instagram) are the most popular platform for the users or consumers where they frequently express their opinion about current topics, various brands, restaurants, movies, books, traveling places etc. Sentiment analysis is a very smart and effective approach to find peoples view about a particular news/ place/restaurant/movie/book/brand. It is beneficial for the both service providers or sellers and consumers. Researchers in the areas of natural language processing, data mining, machine learning, and others have tested a variety of methods of automating the sentiment analysis process. In this research work, I used restaurant reviews dataset to analysis the sentiment and for this approach Gaussian Naïve Bayes method is proposed based on coupling classification methods using arcing classifier and their performances are analyzed in terms of accuracy.
    Keywords
    Machine learning; Sentiment analysis; Opinion analysis; Restaurant reviews; Gaussian Naive Bayes classifier
     
    Description
    This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.
     
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references (pages 31-32).
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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