Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews
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.
product feature extraction consumer review sentiment analysis opinion summarization text mining