N ENHANCED FRAMEWORK FOR SENTIMENT ANALYSIS OF SOCIAL MEDIA CONTENTS USING SUPERVISED LEARNING TECHNIQUES
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Author:
RANA IQRAR AHMAD
Citable URI :
https://vspace.vu.edu.pk/detail.aspx?id=339
Publisher :
Virtual University
Date Issued:
7/4/2020 12:00:00 AM
Abstract
Sentiment analysis or opinion mining is proven to be very effective to analyze huge and complex
amount of text of social media. Social media provides an online environment for the users to
show their behaviors and emotions through tweets and post. Massive amount of personal
information is placed on the World Wide Web due to huge usage of social media. Moods and
emotions of the user are different from each other. Sentiment analysis of any written text
especially social media content is applicable to extract the opinions, emotions and meaningful
insights for better decision making. There are many challenges in the accurate and reliable
sentiment analysis of available social media content. The challenges can be both technical and
theoretical. Machine learning-based sentiment analysis techniques have issues such as huge
lexicon, semantic gap, handling of negation, domain dependency and bi-polar words. Previously,
many machine learning and data mining techniques have been proposed by several researchers to
resolve these issues. However, the existing techniques have failed to provide satisfactory and
reliable results for most of the available datasets.
A novel methodology is proposed to overcome above mentioned issues using better and
simplified way with less computational complexity and high reliability. Data acquisition, feature
encoding, data preprocessing, feature selection, and classification are the various phases of
implemented framework. Data gathering and preprocessing step is very critical in the analysis of
data. The proposed research mainly contributes during data preprocessing, feature encoding, and
classification phases. In feature encoding phase, a hybrid approach of bi-gram and tri-gram is
used for word embedding. In the experiments, several benchmark datasets have been utilized to
evaluate the effectiveness of the proposed framework. The results obtained from the proposed
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methodology show better or at least comparable results with maximum confidence. The outcome
of the proposed work will be helpful to enhance the process of sentiment analysis of social media
contents. The experimental results of the framework will be validated using WEKA simulation
software.
URI :
https://vspace.vu.edu.pk/details.aspx?id=339
Citation:
Ahmad,R(2019),N ENHANCED FRAMEWORK FOR SENTIMENT ANALYSIS OF SOCIAL MEDIA CONTENTS USING SUPERVISED LEARNING TECHNIQUES,VIRTUAL UNIVERSITY OF PAKISTAN.(Lahore,Pakistan)
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Final Version
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