Multi-head attention memory network for short text sentiment classification

Abstract

The invention discloses a multi-head attention memory network for short text sentiment classification. The network comprises a multi-hop memory sub-network, the multi-hop memory sub-network comprises a plurality of independent calculation modules which are connected in sequence, and each independent calculation module comprises a first multi-head attention coding layer, a first linear layer and an output layer which are connected in sequence. The input of each multi-head attention coding layer in the multi-hop memory sub-network comprises original memory and historical information memory, and the multi-head attention memory network learns more complex and abstract nonlinear features contained in a text through stacking conversion of independent calculation modules with enough hop counts; the emotion semantic structure in the text is effectively coded. Furthermore, the original memory of the input multi-hop memory sub-network is fully interacted by the recursive calculation process of the multi-head attention coding layer, so that the remote dependency relationship between the text features is modeled with more components, and the context emotion semantic relationship with higher level is mined, thereby improving the classification performance of the model.

Description

technical field

[0001] The invention relates to the technical field of natural language processing, in particular to a multi-head attention memory network for short text sentiment classification. Background technique

[0002] With the rapid development of Internet technology, social networks and e-commerce platforms have become the most important public information distribution centers. Using the huge data to analyze people's emotions and opinions has important social and scientific value. Sentiment analysis is the computational study of people's opinions, emotions, emotions, evaluations, and attitudes toward products, services, organizations, individuals, issues, events, topics, and their attributes. It is a subtask of text classification. Unlike ordinary text classification, sentiment analysis requires higher-level semantic extraction, which is more technically challenging. How to use natural language processing (natural language processing, NLP) technology to carry out s...

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