Extracting product features from online reviews based on two-level HHMM
Xiaoli Wang, Lu Zhang · 2014
With rapid development of E-commerce, obtaining product features from online reviews effectively is both important consumers and product manufacturers. In this paper, we proposed a two-level Hierarchical Hidden Markov Model (HHMM) to extract product features. In HHMM-1, we use segment tags to divide comment text into Feature-Contained Segment and Non-Feature-Contained Segment. Then the product feature in Non-Feature-Contained Segment is further marked and extracted in HHMM-2. The experimental results of online reviews from Amazon show the HHMM method is very effective in product feature extraction.