%A Longmao HU,Xuegang HU %T Identification of the same product feature based on multi-dimension similarity and sentiment word expansion %0 Journal Article %D 2020 %J Journal of Shandong University(Engineering Science) %R 10.6040/j.issn.1672-3961.0.2019.403 %P 50-59 %V 50 %N 2 %U {http://gxbwk.njournal.sdu.edu.cn/CN/abstract/article_1909.shtml} %8 2020-04-20 %X

Because the existing methods for identifying the same product features were limited by the lack of dictionary coverage or corpus size, an identification method was proposed based on multidimensional similarity and sentiment word expansion. Extracting emotional words of product features through bi-directional long short-term memory and conditional random field (Bi-LSTM-CRF), combining the morpheme similarity, Cilin similarity and term frequency-inverse document frequency (TF-IDF) cosine similarity of product feature words, the same product features were identified by K-medoids clustering algorithm. The experimental results showed that, on mobile and notebook datasets, the maximum adjusted rand index (ARI) reached 0.579 and 0.595 9 respectively, while the minimum entropy reached 0.782 6 and 0.745 7. The proposed method was superior to the adjusted Jaccard similarity combined morpheme, Word2Vec similarity and Word2Vec similarity based on bisecting K-means.