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ORIGINAL ARTICLE
Year : 2018  |  Volume : 3  |  Issue : 2  |  Page : 41-47

Semi-supervised sentiment analysis of consumer reviews


Department of Computer Science, College of Information and Computer Science, Imam Mohammad Bin Saud Islamic University, Riyadh, Saudi Arabia

Correspondence Address:
Dr. Sarah Omar Alhumoud
Department of Computer Science, College of Information and Computer Science, Imam Mohammad Bin Saud Islamic University, Riyadh
Saudi Arabia
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijas.ijas_8_18

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Background: Consumer transactions and individual's online information exchange streams hold within them an enormous amount of data that is large in volume, velocity of generation, and variety. Motivation: Extracting information and trends from these data is a valuable asset in getting a better understanding of consumer's activities and preferences to guide future decision-making. Consequently, analyzing customer reviews through sentiment analysis classification is increasingly growing in interest. However, the resources and lexicons available to aid the classification learning are still scarce. Aim: The present research presents a domain-specific lexicon, enhancing the analysis intelligence of customer reviews on services. The Lexicon for Sentiment Analysis for Reviews (LSAR) is applied using semi-supervised SVM classification. Results: Results were encouraging, showing that the classifier based on the proposed lexicon, LSAR achieved better accuracy 0.94 compared to 0.72 for the AFINN-based classifier.


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