Improve the Accuracy of SenseComp in Thai Consumer’s Review Using Syntactic Analysis | IEEE Conference Publication | IEEE Xplore

Improve the Accuracy of SenseComp in Thai Consumer’s Review Using Syntactic Analysis


Abstract:

Thai sentiment analysis can be analyzed in multiple dimensions: product, price, and shipping. The scores from sentiment analysis can be considered as multi-dimensional tr...Show More

Abstract:

Thai sentiment analysis can be analyzed in multiple dimensions: product, price, and shipping. The scores from sentiment analysis can be considered as multi-dimensional trust score of e-vendor which affects the purchase decision of the consumer. A consumer's review that has different sentiments describing the same dimension in the sentence leads to the incorrect scoring problem in SenseComp - the method to automatically analyze Thai sentiment in the consumer's review in multiple dimensions with sentiment compensation technique. In this paper, SenseComp using syntactic analysis method (SenseComp2) is proposed in order to improve the accuracy of the original SenseComp. Thai syntactic structure in the consumer's review is analyzed before the scores in multiple dimensions are given. The results show that SenseComp2 increases the accuracy of the original SenseComp up to 31.88% in the sentiment analysis of Thai consumer's review when there are multiple dimensions and various sentiments in the same sentence.
Date of Conference: 10-13 July 2019
Date Added to IEEE Xplore: 13 January 2020
ISBN Information:
Conference Location: Pattaya, Thailand
References is not available for this document.

I. Introduction

In social commerce, good ratings and reviews of a product can increase the consumer’s trust which in turn influences consumer’s purchase intention [1]. To know the opinion of Thai consumers in the social commerce, Thai sentiment analysis or opinion mining can be used to automatically analyze the sentiments or opinions in Thai consumers’ reviews [2]. Trustworthiness of an e-vendor in Thailand can be measured in multiple dimensions such as product, price, and shipping dimensions. The consumer’s review can be automatically analyzed in multiple dimensions by using SenseComp method. In SenseComp, the sentiment in consumer’s review is analyzed by using multi-dimensional lexicon and sentiment compensation technique. The scores 1, -1, and 0 are given to the positive, negative, and neutral sentiments in the review respectively. The scores can be considered as the multi-dimensional trust score of e-vendor in e-marketplace and social commerce [3]. The weak point of SenseComp is the inability to give the correct scores to the reviews that have multiple dimensions and various sentiments in the same sentence. This is because there may be different sentiments describing the same dimension in the sentence.

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