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Text sentiment analysis method and system based on deep learning

A sentiment analysis and deep learning technology, applied in neural learning methods, semantic analysis, special data processing applications, etc., can solve problems such as large manual workload, and achieve the effect of reducing labor costs, accurate classification results, and accurate results

Inactive Publication Date: 2018-06-01
北京牡丹电子集团有限责任公司数字科技中心
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  • Application Information

AI Technical Summary

Problems solved by technology

Rule-based methods are generally composed of manually defined rule bases and depths. This method is generally effective, but the manual workload is heavy; learning-based methods are mostly based on traditional machine learning methods such as SVM and Naive Bayes etc., depending on feature engineering, it is necessary to manually find data features, and the quality of feature engineering directly affects the final classification effect

Method used

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  • Text sentiment analysis method and system based on deep learning
  • Text sentiment analysis method and system based on deep learning

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Embodiment Construction

[0059] In the following description, for purposes of illustration rather than limitation, specific details, such as specific device structures, interfaces, and techniques, are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0060] figure 1 A schematic flowchart of a text sentiment analysis method based on deep learning provided by Embodiment 1 of the present invention is given. Such as figure 1 As shown, the subject of execution of the method may be a server, and the method includes the following steps:

[0061] Step 1, collecting initial text data for training, and normalizing the initial text data to generate pre...

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Abstract

The invention particularly relates to a text sentiment analysis method and system based on deep learning. The method includes: normalizing initial text data to generated preprocessed text data, and clustering the preprocessing text data to preset fields; manually labeling part of data in different fields, training a sentiment analysis model based on the deep learning, and building the special depth of each preset field; using a formed classifier and the special depths to perform sentiment classification on to-be-classified text. The method has the advantages that manpower cost is reduced, influence of feature engineering on classification results is avoided, and workload of special engineering is reduced at the same time; in addition, the fields where the text belongs to are considered, sothat the accuracy of text sentiment analysis is increased.

Description

technical field [0001] The present invention relates to the field of natural language processing, in particular to a text sentiment analysis method and system based on deep learning. Background technique [0002] In the Web 2.0 era, every netizen has become a source of information on the Internet. Information release platforms for various purposes emerged as the times require, such as FaceBook, Xiaonei, Sina Weibo, etc., for users to publish, obtain, and share various information. Due to the large number of Internet users, the average amount of information generated by each information release platform every day is also large, so the amount of information generated by the Internet every day is also huge. Sentiment analysis, also known as emotion mining and opinion mining, is the process of processing, analyzing, summarizing, and inferring texts to obtain the emotional color of the text. It is also very difficult. [0003] In terms of text sentiment analysis, foreign schol...

Claims

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Application Information

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IPC IPC(8): G06F17/27G06F17/30G06N3/04G06N3/08
CPCG06F16/355G06N3/084G06F40/30G06N3/045
Inventor 王家彬柳宜江
Owner 北京牡丹电子集团有限责任公司数字科技中心
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