Abstract
The fine-grained affective analysis of product reviews is a fine-grained mining of the content of reviews, which has important research significance. The explicit attribute-viewpoint extraction is one of its key research issues. Due to the complex structure and colloquial features of online product reviews, traditional models are not ideal for the extraction of explicit attribute-views pairs. To solve this problem, this paper proposes an extraction method based on deep learning and integrating positional relationship information. By using the Bi-directional Long Short-Term Memory Network to overcome the problem of long distance dependence, the author makes full use of the context, combines the attention mechanism to reduce the noise weight of sentence-level informal text, and integrates the feature of position relation to enrich the feature information. The experimental results show that compared with other models, the network model proposed in this paper has improved the call rate and F1 value.
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