ICTACT Journal on Soft Computing

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Student Feedback Sentiment Analysis System for Distance Education using Arm with K-Means Clustering


Affiliations
1 Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, India
     

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The rapid development of Internet has resulted in the boom of evaluations about products and services. For extracting the aspects and determining the opinions from reviews Sentiment Analysis is used. The main challenges faced by Sentiment Analysis system is that, in order to increase or decrease the market value of the product the spammers may post irrelevant or fake reviews and another challenge deals with the classification of both Implicit and Explicit features present among the review sentences in the dataset. The proposed system deals with the identification of fake reviews through fake review Indicators which help in removing the fake reviews. For the better identification of both implicit and explicit features, association rule mining with K-Means Clustering is used. Lexicon method is used for the classification of sentiments into positive and negative polarities. The advantage of the proposed system is that the fake reviews can be detected and eliminated in the dataset and both implicit and explicit attribute extraction from the review sentence can be identified along with its polarities through Lexicon based Method.

Keywords

Lexicon Based Method, Sentiment Analysis, Spammers, K-Means Clustering.
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  • Student Feedback Sentiment Analysis System for Distance Education using Arm with K-Means Clustering

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Authors

G. N. Harshini
Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, India
N. Gobi
Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, India

Abstract


The rapid development of Internet has resulted in the boom of evaluations about products and services. For extracting the aspects and determining the opinions from reviews Sentiment Analysis is used. The main challenges faced by Sentiment Analysis system is that, in order to increase or decrease the market value of the product the spammers may post irrelevant or fake reviews and another challenge deals with the classification of both Implicit and Explicit features present among the review sentences in the dataset. The proposed system deals with the identification of fake reviews through fake review Indicators which help in removing the fake reviews. For the better identification of both implicit and explicit features, association rule mining with K-Means Clustering is used. Lexicon method is used for the classification of sentiments into positive and negative polarities. The advantage of the proposed system is that the fake reviews can be detected and eliminated in the dataset and both implicit and explicit attribute extraction from the review sentence can be identified along with its polarities through Lexicon based Method.

Keywords


Lexicon Based Method, Sentiment Analysis, Spammers, K-Means Clustering.

References