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A user emotion analysis method based on deep learning
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A technology of sentiment analysis and deep learning, applied in the field of sentiment analysis, can solve problems that cannot be effectively solved, and achieve the effects of reducing training time, improving overall speed, and strong practicability
Active Publication Date: 2019-04-26
ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
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[0003]Traditional methods for solving sentiment analysis problems include unsupervised methods based on sentiment dictionaries and artificial judgment rules, supervised methods based on machine learning, and These methods can achieve certain results when the data size is large or the semantics are not rich enough, but as the amount of data becomes larger and the expression methods become more and more abundant, traditional methods can no longer effectively solve this type of problem, and new methods are urgently needed. propose
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Embodiment 1
[0024] The present invention provides a technical solution: a user sentiment analysis method based on deep learning, comprising the following steps;
[0025] Step 1. Build a vocabulary, pre-train the text words in the corpus, construct a feature vector acquisition model according to the nature of the analysis object, and obtain the corresponding TF-IDF and Word2vec feature word vectors;
[0026] Step 2, use the classifier to classify and select a part as the seed dictionary, and give the emotional polarity score dictionary of these seed corpora, improve the corresponding TF-IDF feature selection process, and obtain a new improved TF-IDF feature selection word vector;
[0027] Step 3. Save the newly improved TF-IDF feature word vector and the Word2vec feature word vector results respectively;
[0028] Step 4, adding and averaging the emotional seed words of each emotional classification to obtain the polarity probability of the central word vector of each emotion;
[0029] S1....
Embodiment 2
[0036] The present invention provides a technical solution: a user sentiment analysis method based on deep learning, comprising the following steps;
[0037] Step 1. Build a vocabulary, pre-train the text words in the corpus, construct a feature vector acquisition model according to the nature of the analysis object, and obtain the corresponding TF-IDF and Word2vec feature word vectors;
[0038] Step 2, use the classifier to classify and select a part as the seed dictionary, and give the emotional polarity score dictionary of these seed corpora, improve the corresponding TF-IDF feature selection process, and obtain a new improved TF-IDF feature selection word vector;
[0039] Step 3. Save the newly improved TF-IDF feature word vector and the Word2vec feature word vector results respectively;
[0040] Step 4, adding and averaging the emotional seed words of each emotional classification to obtain the polarity probability of the central word vector of each emotion;
[0041] S1....
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Abstract
The invention discloses a user emotion analysis method based on deep learning. The method comprises following steps of Constructing a vocabulary; Using a classifier to classify and select one part asa seed dictionary, giving emotion polarity score dictionaries of the seed corpora, and improving the corresponding TF-IDF feature selection process; Storing the result of the improved TF-IDF feature word vector and the result of the Word2vec feature word vector; Summing and averaging the emotion seed words of each emotion classification to obtain the probability of the polarity of the central wordvector of each emotion. The invention provides a user emotion analysis method based on deep learning. and word frequency information, emotion information and semantic information of the text are effectively combined by combining a basic word frequency feature selectionalgorithm and a Word2vec algorithm, and a comparison test is carried out on a newly constructed emotion semantic word vector andan original word vector, so that the effectiveness of the emotion semantic word vector is effectively proved.
Description
technical field [0001] The invention relates to the technical field of sentiment analysis, in particular to a user sentiment analysis method based on deep learning. Background technique [0002] As a platform for people's information exchange and resource sharing, the Internet saves a large amount of data containing subjective information. How to extract people's interests, texts with opinions, and sentiment classification from these massive data is one of the current research hotspots. one; [0003] Traditional methods for solving sentiment analysis problems include unsupervised methods based on sentiment dictionaries and human judgment rules, and supervised methods based on machine learning. These methods can achieve certain results when the amount of data is not large or the semantics are not rich enough. , but as the amount of data becomes larger and the expression methods become more and more abundant, the traditional methods can no longer effectively solve this kind o...
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