Aspect-level sentiment classification method

A sentiment analysis and aspect technology, applied in the field of artificial intelligence, can solve problems such as unsatisfactory effect of cross-domain sentiment analysis, poor sentiment analysis effect, ignoring aspect words and contextual interaction information, etc., to achieve ideal text sentiment analysis accuracy, improve The effect of the affective discriminant effect

Pending Publication Date: 2021-12-31
XIAN UNIV OF POSTS & TELECOMM
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Problems solved by technology

However, regardless of artificial construction or automatic construction of sentiment dictionaries, it is often only for a certain field, and the effect of cross-domain sentiment analysis is not ideal.
At present, the research on aspect-l

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  • Aspect-level sentiment classification method
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  • Aspect-level sentiment classification method

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Embodiment Construction

[0032] Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0033] Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities ...

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Abstract

The embodiment of the invention relates to an aspect-level sentiment classification method. The method comprises the following steps: extracting feature information of aspect words and context of a to-be-analyzed text by adopting a bidirectional long-short-term memory network model to respectively obtain hidden layer representation of the aspect words and context hidden layer representation of the context; for the obtained hidden layer representation and context hidden layer representation, extracting context emotion feature information in direct grammatical relation with aspect words by adopting a graph convolutional network according to a syntax dependency tree, and obtaining context information feature representation fused with grammar dependency information; based on context information feature representation, utilizing an attention mechanism to learn interaction information of aspect words and the context, extracting emotion feature information making important contribution to aspect word emotion classification in the context, and predicting emotion polarity. Compared with a conventional classical text sentiment analysis method and a current mainstream method, the method provided by the embodiment of the invention has more ideal text sentiment analysis accuracy.

Description

technical field [0001] The present disclosure relates to the technical field of artificial intelligence, in particular to an aspect-level sentiment classification method. Background technique [0002] With the rise of social media, sentiment analysis has increasingly become a research hotspot in the direction of natural language processing in the field of artificial intelligence. Its research objects are people's opinions, emotions and attitudes towards services, events, products, topics and their attributes. Analysis of speech can often quickly grasp people's attitudes or opinions on hot topics, policies, commodities, etc., which has significant research value. [0003] In related technologies, early sentiment analysis methods mainly rely on statistical methods to establish sentiment dictionaries, and use the sentiment dictionary as the basis of sentiment analysis. However, regardless of artificial construction or automatic construction of sentiment dictionaries, it is oft...

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Application Information

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IPC IPC(8): G06F16/35G06F40/242G06F40/30G06N3/04G06N3/08
CPCG06F16/35G06F40/30G06F40/242G06N3/08G06N3/047G06N3/044G06N3/045
Inventor 孔韦韦王泽
Owner XIAN UNIV OF POSTS & TELECOMM
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