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Film review emotion tendency analysis algorithm

A technology of emotional tendency and analysis algorithm, applied in the field of natural language processing, can solve the problem of low accuracy of overall emotional tendency judgment, and achieve the effect of improving the accuracy

Active Publication Date: 2018-10-30
DALIAN NATIONALITIES UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

According to the difference in the emotional polarity of the emotional words in the dictionary, the emotional dictionary is divided into a positive dictionary and a derogatory dictionary. According to the polarity and emotional intensity of the emotional words in the dictionary, the emotional score of the entire sentence is calculated, and finally the emotional tendency of the sentence is obtained. However, this method is not very accurate in judging the overall emotional orientation of a film review with an equal amount of emotional words with different polarities.

Method used

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  • Film review emotion tendency analysis algorithm

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Experimental program
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Embodiment 1

[0032] This embodiment is aimed at the emotional tendency analysis of Chinese film reviews, and it proposes a method for discriminating emotional tendency, mainly including training methods, testing methods, and analysis methods. The program uses machine learning to extract feature words and convert text into features The representation form and the classifier are constructed by naive Bayesian thinking, and the feature extraction adopts part-of-speech selection to avoid not extracting meaningful features due to the lack of movie reviews.

[0033] The technical scheme disclosed in this embodiment is as follows:

[0034] A method for analyzing the emotional tendency of film reviews based on machine learning, comprising the following steps:

[0035] Step 1: Write a crawler to download Douban movie reviews, and the downloaded movie reviews form a corpus;

[0036] Step (a): Get the URL of the movie to be downloaded in Douban.

[0037] Step (b): Download the movie review, movie na...

Embodiment 2

[0102] As an example supplement to the technical solution in Example 1, figure 1 The process flow of the analysis method of the present invention is shown. In this embodiment, jieba word segmentation is used to segment a large number of texts and select specific part-of-speech words, and use jieba word segmentation to extract the stem word of the sentence, take the union of the two, and evaluate the downloaded movie reviews according to their ratings. Classification, including positive and negative categories. And convert the movie review text into the form of feature representation, use the classification algorithm to build a classifier, and then perform necessary post-processing. Let's take a movie review in the data set as an example, combined with figure 1 The present invention will be described in detail.

[0103] Step 1. Download movie reviews, write a crawler to download movie reviews from Douban Movies. For example, one of the movie reviews downloaded is as follows:...

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Abstract

The invention discloses a film review emotion tendency analysis algorithm and belongs to the field of natural language processing. The accuracy problem of a film review emotion analysis machine learning algorithm is solved. The key point is that emotion tendency classification probability calculation is carried out on to-be-tested film reviews through utilization of a classifier determined by thefollowing mathematical model shown in the specification. An effect of improving the analysis accuracy is achieved.

Description

technical field [0001] The invention belongs to the field of natural language processing and relates to an algorithm for analyzing the emotional tendency of film reviews. Background technique [0002] In various forums, shopping sites, review sites, Weibo, etc., more and more users express their opinions, opinions, attitudes, and emotions on them. If the user's emotional change process can be analyzed, then these comments will be Provide us with a lot of information. For example, reviews of a movie, reviews of a product, etc. Based on the analysis of subjective text with emotional color, the user's attitude is identified, whether it is like, dislike, or neutral. There are many applications in real life, such as predicting stock trends, movie box office, election results, etc. through emotional analysis of Weibo users. It can also be used to understand users’ preferences for companies and products. The analysis results can be used to Improve products and services, and disc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 高宠赵丹丹
Owner DALIAN NATIONALITIES UNIVERSITY