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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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    Aspect-based sentiment analysis using SemEval and Amazon datasets

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    17141017,13301117_CSE.pdf (2.193Mb)
    Date
    2017
    Publisher
    BRAC University
    Author
    Hasib, Tamanna
    Rahin, Saima Ahmed
    Metadata
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    URI
    http://hdl.handle.net/10361/9542
    Abstract
    Sentiment analysis has become one of the most important tools in natural language processing, since it opens many possibilities to understand people’s opinions on different topics. Aspect-based sentiment analysis aims to take this a step further and find out, what exactly someone is talking about, and if he likes or dislikes it. Real world examples of perfect areas for this topic are the millions of available customer reviews in online shops. There have been multiple approaches to tackle this problem, using machine learning, deep learning and neural networks. However, currently the number of labelled reviews for training classifiers is very small. Therefore, we undertook multiple steps to research ways of improving ABSA performance on small datasets, by comparing recurrent and feed-forward neural networks and incorporating additional input data that was generated using different readily available NLP tools.
    Keywords
    Sentiment analysis; SemEval; Amazon dataset; Dependency parsing; Word vectors; Opinion mining
     
    Description
    This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.
     
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references (pages 35-37).
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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