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A text emotion analysis method based on bi-directional interactive neural network

A neural network and sentiment analysis technology, applied in text database clustering/classification, semantic analysis, unstructured text data retrieval, etc., can solve the problem of not using location information, and achieve the effect of strong discrimination ability

Pending Publication Date: 2019-03-26
TIANJIN UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

However, most of the current methods only use global word vector information and do not use location information.

Method used

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  • A text emotion analysis method based on bi-directional interactive neural network
  • A text emotion analysis method based on bi-directional interactive neural network
  • A text emotion analysis method based on bi-directional interactive neural network

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Embodiment Construction

[0045] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following description. figure 1 Shows the flow of the entity-text sentiment analysis method proposed by this method; figure 2 The neural network model designed by the present invention is shown; image 3 The comparison results of sentiment classification between the final different algorithms are shown. Specific steps are as follows:

[0046](1): Based on the famous emotional dictionary SentiWordNet, select 127 keywords containing positive and negative emotions to form an emotional vocabulary, in which the number of positive emotional words is 62, such as happy (happy), smiling (smiling), etc., negative The number of emotional words is 65, such as sad (sorrow), murder (murder) and so on.

[0047] (2): Based on the Flickr platform to collect data and establish a...

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Abstract

The invention discloses a text emotion analysis method based on a bi-directional interactive neural network, comprising the following steps: collecting entities; Text emotion corpus set, which is divided into training set and test set; Preprocessing the entities and texts in the corpus; Relative position information and adopting global word vector information to construct word and sentence representations. The entity and text word vectors of the training corpus are inputted into the neural network, training and the affective classification model. Inputting The test set entity and text word vector into the neural network model, and calculating the prediction probability of each sample. Using quantum-heuristic multi-modal decision fusion method, the weighted fusion of text prediction probability and image prediction probability is used to obtain more accurate and intelligent multi-modal emotion classification results.

Description

technical field [0001] The invention relates to the technical field of text emotion classification, in particular, to a text emotion analysis method based on a two-way interactive neural network. Background technique [0002] With the rapid development of the Internet and social networks, more and more users like to post comments and share their opinions on social platforms (such as Weibo, Comments, Facebook, etc.), becoming one of the main sources of information for users in their daily lives. one. Different from pure text sentiment analysis, the texts that appear on social media now generally not only describe one thing, but usually turn into evaluations of one or more things, so our sentiment analysis of texts must be specific to a certain Sentiment polarity in the case of entities, which allows us to know what the sentiment of each thing described by a piece of text is. Therefore, text sentiment analysis not only has important theoretical significance, but also contain...

Claims

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Application Information

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IPC IPC(8): G06F17/27G06F16/35
CPCG06F40/295G06F40/30
Inventor 张立鹏顾淑琴张鹏
Owner TIANJIN UNIV
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