Santhiran, Rajeswary and Varathan, Kasturi Dewi and Chiam, Yin Kia (2024) Feature extraction from customer reviews using enhanced rules. PeerJ Computer Science, 10. e1821. ISSN 2376-5992, DOI https://doi.org/10.7717/peerj-cs.1821.
Full text not available from this repository.Abstract
Opinion mining is gaining significant research interest, as it directly and indirectly provides a better avenue for understanding customers, their sentiments toward a service or product, and their purchasing decisions. However, extracting every opinion feature from unstructured customer review documents is challenging, especially since these reviews are often written in native languages and contain grammatical and spelling errors. Moreover, existing pattern rules frequently exclude features and opinion words that are not strictly nouns or adjectives. Thus, selecting suitable features when analyzing customer reviews is the key to uncovering their actual expectations. This study aims to enhance the performance of explicit feature extraction from product review documents. To achieve this, an approach that employs sequential pattern rules is proposed to identify and extract features with associated opinions. The improved pattern rules total 41, including 16 new rules introduced in this study and 25 existing pattern rules from previous research. An average calculated from the testing results of five datasets showed that the incorporation of this study's 16 new rules significantly improved feature extraction precision by 6%, recall by 6% and F -measure value by 5% compared to the contemporary approach. The new set of rules has proven to be effective in extracting features that were previously overlooked, thus achieving its objective of addressing gaps in existing rules. Therefore, this study has successfully enhanced feature extraction results, yielding an average precision of 0.91, an average recall value of 0.88, and an average F -measure of 0.89.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Opinion mining; Sentiment analysis; Aspect extraction; Product review; Customer review; Pattern-based rule |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Information System Faculty of Computer Science & Information Technology > Department of Software Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 11 Nov 2024 01:44 |
| Last Modified: | 11 Nov 2024 01:44 |
| URI: | http://eprints.um.edu.my/id/eprint/45720 |
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