Feature extraction of search product based on multi-feature fusion-oriented to Chinese online reviews

The increasing Chinese online reviews contain rich product demand information, especially for search products. This study suggests a product feature extraction model from online reviews based on multi-feature fusion named PFEMF (products features extraction based on multi-feature fusion) model. Combining sentence and word characteristics of Chinese online reviews, the model explores the lexical features, frequency features, span features, and semantic similarity features of words. And then, they are fused to identify the features that customers are concerned about most by sequential relationship analysis. The identified product feature provides direction for product innovation and facilitates the product selection for customers. Finally, the study takes iPad Air as an example to prove this model. The results show that the extraction performance of the PFEMF model is superior to the traditional term frequency-inverse document frequency (tf-idf) algorithm, word span algorithm, and semantic similarity algorithm.