Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews |
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Authors: | Chih-Ping Wei Yen-Ming Chen Chin-Sheng Yang Christopher C Yang |
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Affiliation: | (1) Institute of Service Science, College of Technology Management, National Tsing Hua University, Hsinchu, Taiwan, ROC;(2) Department of Information Management, College of Management, National Sun Yat-Sen University, Kaohsiung, Taiwan, ROC;(3) Department of Information Management, College of Informatics, Yuan-Ze University, Chung-Li, Taiwan, ROC;(4) College of Information Science and Technology, Drexel University, Philadelphia, PA, USA |
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Abstract: | The Web has become an excellent source for gathering consumer opinions (more specifically, consumer reviews) about products.
Consumer reviews are essential for retailers and product manufacturers to understand the general responses of customers to
their products and improve their marketing campaigns or products accordingly. In addition, consumer reviews enable retailers
to recognize the specific preferences of each customer, which facilitates effective marketing decisions. As the number of
consumer reviews expands, it is essential and desirable to develop an efficient and effective sentiment analysis technique
that is capable of extracting product features stated in consumer reviews (i.e., product feature extraction) and determining
the sentiments (positive or negative semantic orientations) of consumers for these product features (i.e., opinion orientation
identification). Product feature extraction is critical to sentiment analysis, because its effectiveness significantly affects
the performance of opinion orientation identification, as well as the ultimate effectiveness of sentiment analysis. Therefore,
this study concentrates on product feature extraction from consumer reviews. Specifically, we propose a semantic-based product
feature extraction (SPE) technique that exploits a list of positive and negative adjectives defined in the General Inquirer
to recognize opinion words semantically and subsequently extract product features expressed in consumer reviews. Using a prevalent
product feature extraction technique and the SPE-GI technique (a variant of SPE) as performance benchmarks, our empirical
evaluation shows that the proposed SPE technique outperforms both benchmark techniques. |
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