Character-based hierarchical text sentiment analysis method and system

Abstract

The invention discloses a character-based hierarchical text sentiment analysis method and system, and the method comprises the steps: carrying out the preprocessing of given text data, wherein the preprocessing comprises designing a character set, dividing sentences in a text, and obtaining a text representation in a digital form based on the character set; establishing a character-level neural network model: inputting the preprocessed text data into the character-level neural network model, sequentially passing through a model embedding layer, a convolutional neural network layer and a decoding layer, and extracting and outputting a feature vector of each sentence in the text; and establishing a sentence-level neural network model: taking output of the character-level network as input, and outputting probability distribution of sentiment classification of the text through a recurrent neural network layer, an attention layer and a decoding layer in sequence. The initial features of thetext are extracted from the character level, the sentence level network contains the time sequence information, the network can tend to sentences beneficial to the sentiment analysis result, and theaccuracy and robustness of the model are improved.

Description

technical field [0001] The invention relates to the technical field of emotion analysis of natural language processing, in particular to a character-based hierarchical text emotion analysis method and system. Background technique [0002] With the huge increase in the amount of Internet information in recent years, people can access a large amount of text information, such as news, blogs, comments, etc., through terminals such as mobile phones and computers. Extracting important information from a large number of texts, such as text summaries and emotional tendencies, has become an urgent need to quickly understand information in the era of information explosion. Among them, emotional orientation, as a higher-level abstraction of text information, has important application value. The character-based hierarchical text sentiment analysis method with attention mechanism provides an efficient solution for extracting emotional tendencies from a large number of texts, which can h...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the defects and deficiencies in the prior art, the present invention provides a character-based hierarchical text sentiment analysis method and system, aiming at the huge number of words and the flexibility problem and the relationship between words faced by the word-based text sentiment analysis method. problems, low-frequency words and oov problems, and the problem that character-based models are prone to overfitting and poor model robustness. A character-based network is designed, and a sentence-level network is added to the character-level network, and a The character-based neural network is different from the existing similar methods. The present invention considers that multi-group thinking can usually play a better effect in natural language processing, and greatly improves the character-level neural network, making The feature extraction effect of the network on the text is better

[0005] However, due to the diversity of character combinations and the characteristics of convolutional networks, character-based models are prone to overfitting and poor model robustness.

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