A comprehensive analysis of adverb types for mining user sentiments on Amazon product reviews
Article
Article Title | A comprehensive analysis of adverb types for mining user sentiments on Amazon product reviews |
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ERA Journal ID | 32110 |
Article Category | Article |
Authors | Chauhan, Ummara Ahmed (Author), Afzal, Muhammad Tanvir (Author), Shahid, Abdul (Author), Abdar, Moloud (Author), Basiri, Mohammad Ehsan (Author) and Zhou, Xujuan (Author) |
Journal Title | World Wide Web |
Journal Citation | 23 (3), pp. 1811-1829 |
Number of Pages | 19 |
Year | 2020 |
Publisher | Springer |
Place of Publication | New York, United States |
ISSN | 1386-145X |
1573-1413 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s11280-020-00785-z |
Web Address (URL) | https://link.springer.com/article/10.1007%2Fs11280-020-00785-z |
Abstract | Online shopping websites like Amazon stipulate a platform to the users where they can share their opinions about different products. Recently, it has been identified that prior to the purchasing, 81% of the users explore different online platforms in order to assess the reliability of product that they intend to buy. The reviews of different users are expressed by using natural language, which help a user to make an informed decision. From past few years, scientific community has payed attention to automatically specify the meaning of review through Sentiment Analysis. Sentiment Analysis is a research area which is gradually being evolved thus, helping the users to tackle the sentiment hidden in a review. To date, different sentiment analysis-based studies have been conducted in literature. For sentiment classification, the core ingredient is the exploitation of polarity bearing words present in the reviews e.g. adjectives, verbs, and adverbs etc. Different studies suggest the importance of different forms of adverbs in sentiment classification task. In literature, it has been reported that general adverbs strongly help to classify sentiments with better accuracy whereas other suggest that degree adverbs are important for sentiment classification. There are ten distinct forms of adverbs such as general adverbs, general superlative adverbs, general comparative adverbs, general-wh adverbs, degree adverbs, degree superlative adverbs, degree comparative adverbs, degree-wh adverbs, time adverbs and locative adverbs. In this paper, we intend to tackle a question that what is the impact of different forms of adverb on the classification of sentiments? For this, the impacts of all these forms have been evaluated on 51,005 reviews of two products, office products and musical DVDs acquired from Amazon. The outcomes of study revealed that two general superlative adverbs and degree-wh adverb hold more impact than the other forms of adverbs. The general superlative adverbs have attained F-measure of 0.86 and degree-wh adverbs have attained F-measure of 0.80. |
Keywords | big data, forms of adverb, product reviews, role of adverbs, sentiment classification |
ANZSRC Field of Research 2020 | 460999. Information systems not elsewhere classified |
Byline Affiliations | Capital University of Science and Technology, Pakistan |
Kohat University of Science and Technology, Pakistan | |
University of Montreal, Canada | |
Shahrekord University, Iran | |
School of Management and Enterprise | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5888/a-comprehensive-analysis-of-adverb-types-for-mining-user-sentiments-on-amazon-product-reviews
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