A text sentiment analysis method based on a bidirectional long-short term memory neural network

Abstract

The invention belongs to the crossing field of artificial intelligence and data mining, and particularly relates to a text sentiment analysis method based on a bidirectional long-short term memory neural network. The method comprises the following steps: in a text mapping word vector mapping framework, expressing a text as a word vector matrix; Constructing an improved convolutional neural network, and performing feature extraction on the mapped word vector matrix; Training the extracted features by using a bidirectional long-short term memory recurrent neural network until the loss function is minimum; And adding a global mean pooling layer and a classification layer behind the recurrent neural network so as to output sentiment classification of the text. According to the method, deep learning is applied to text sentiment analysis, the limitation of manual extraction of text sentiment characteristics is avoided, the advantages of a convolutional neural network and a recurrent neural network are combined in the field of deep learning, and the network is improved, so that the method can be better applied to a text processing task.

Description

technical field [0001] The invention belongs to the intersecting field of artificial intelligence and data mining, and in particular relates to a text emotion analysis method based on a bidirectional long-short-term memory neural network. Background technique [0002] In recent years, deep learning has been more and more widely used in text classification processing. Text classification aims to automatically process text information, so as to quickly and accurately analyze massive texts. Due to the rapid development of the Internet, more and more subjective information appears on the Internet, so it is of great significance to conduct sentiment analysis on users' subjective information to grasp the user's value trend. As an important branch of text classification, sentiment analysis has become a research hotspot in the fields of natural language processing, data mining, and information retrieval. [0003] Traditional text sentiment analysis methods mainly use the Bag of Wo...

AI Technical Summary

Problems solved by technology

[0007] Using a single deep learning model will have the following disadvantages: (1) CNN can acquire the ability to capture local features, but lacks the ability to learn sequential sequences; (2) RNN can model sequential sequences, but cannot extract them in parallel. characteristics of the text

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