Title :
Extracting product features from online reviews based on two-level HHMM
Author :
Xiaoli Wang ; Zhang Lu
Author_Institution :
Sch. of Econ. & Manage., Tongji Univ., Shanghai, China
Abstract :
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.
Keywords :
electronic commerce; feature extraction; hidden Markov models; Amazon; HHMM-1; HHMM-2; e-commerce; nonfeature-contained segment; online reviews; product feature extraction; segment tags; two-level HHMM; two-level hierarchical hidden Markov model; Data mining; Educational institutions; Feature extraction; Hidden Markov models; Information retrieval; Maximum likelihood estimation; Training; HHMM; data mining; product feature extraction;
Conference_Titel :
Computer & Information Technology (GSCIT), 2014 Global Summit on
Conference_Location :
Sousse
Print_ISBN :
978-1-4799-5626-5
DOI :
10.1109/GSCIT.2014.6970125
