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Identification of Product Innovation Features Based on At-LSTM
YAN Kang， HUANG Xun-jiang， ZHANG Qiang， WANG Deng
2022, 43 (10):
1506-1512.
DOI: 10.12068/j.issn.1005-3026.2022.10.019
In view of the lack of contextual information， insufficient capture of important contents， big noise， and text-level coarse-grained sentiment analysis in the previous research of the online review information mining， a framework model of LSTML (long short term memory) product innovation feature identification based on attention mechanism was presented. Through the screening of reviews available， the construction of product feature lexicon and emotion lexicon， the construction of At-LSTM (attention based LSTM) sentiment analysis model， and the combination of fine-grained emotions and the Kano model， the clear directions for product improvement were provided. The experiments on smart phone reviews on JD.com and Taobao showed that the accuracy， precision， and recall rates of the At-LSTM model are 91.52%， 91.73% and 91.53%， respectively， which are significantly improved compared with the other models such as KNN， NB and SVM. The classification of different demand levels of product features also facilitates the innovation and improvement of mobile phone products.
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