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Extracting product features and opinion words using pattern knowledge in customer reviews.
Su Su Htay1, Khin Thidar Lynn2
1University of Computer Studies, Mandalay, Myanmar.
Thescientificworldjournal
|January 25, 2014
Summary
This study introduces an efficient method for opinion mining from customer reviews, extracting product features and associated opinions to aid user and merchant decision-making in e-commerce.
Area of Science:
- Natural Language Processing
- Information Retrieval
- Computational Linguistics
Background:
- E-commerce growth necessitates analyzing customer feedback.
- Customer reviews (feedback data) offer insights into product/service opinions.
- Automated opinion mining is crucial for understanding large volumes of review data.
Purpose of the Study:
- To propose an efficient method for opinion extraction from customer reviews.
- To identify opinion words/phrases linked to specific product features.
- To facilitate automated opinion mining applications.
Main Methods:
- Developing a novel approach for opinion extraction.
- Analyzing review text to identify opinion patterns.
- Utilizing adjectives, adverbs, verbs, and nouns to capture opinions on features.
Main Results:
- Successfully extracted features and associated opinions from customer reviews.
- Demonstrated an efficient method for opinion word/phrase identification.
- The approach effectively captures opinion expressions within review text.
Conclusions:
- The proposed method efficiently extracts product features and opinions from customer reviews.
- Extracted data aids in generating informative summaries for users and merchants.
- Supports informed decision-making for product selection and tracking.
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