KFU. Card publication. Solving the Problem of Sentiment Analysis Using Neural Network Models.

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

SOLVING THE PROBLEM OF SENTIMENT ANALYSIS USING NEURAL NETWORK MODELS.

Form of presentation

Articles in international journals and collections

Year of publication

2020

Язык

русский

Authors, employees of KFU

Gafarov Fail Mubarakovich, author

Sitdikova Farida Bizyanovna, author

Authors, students KFU

Akhmetgaliev Aynur Islamovich, author

Bibliographic description in the original language

A.I. Akhmetgaliev, F.M.Gafarov, F.B.Sitdikova. Solving the Problem of Sentiment Analysis Using Neural Network Models. // International Journal of Pharmaceutical Research, Jan - March 2020, Vol 12, Issue 1, pp. 850-855. ISSN-0975-2366

Annotation

The article considers methods that create a vector representation of words in the n-dimensional vector space in order to solving the problem of sentiment analysis based on neural network models of natural language processing . The methods are based on «Word2Vec«, «GloVe«, «FastText« technology. Approaches are used in the tasks of classification, sentiment analysis, typo correction, recommendation systems. We present the results of classifications comparison in the problem of sentiment analysis of a multilayer perceptron, a convolutional and recurrent neural network, decision trees (random forest), support vector machine (SVM), naive Bayes classifier (NB), and k-nearest neighbors (K-NN). The results of the classification are presented for three data sets: Twitter messages, reviews of various goods and services, Russian-language news.

Keywords

sentiment analysis, Word2Vec, GloVe, FastText, vector word representation, recurrent neural networks, convolutional neural networks

The name of the journal

International Journal of Pharmaceutical Research

URL

https://doi.org/10.31838/ijpr/2020.12.01.162

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=221637&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Gafarov Fail Mubarakovich	ru_RU
dc.contributor.author	Sitdikova Farida Bizyanovna	ru_RU
dc.contributor.author	Akhmetgaliev Aynur Islamovich	ru_RU
dc.date.accessioned	2020-01-01T00:00:00Z	ru_RU
dc.date.available	2020-01-01T00:00:00Z	ru_RU
dc.date.issued	2020	ru_RU
dc.identifier.citation	A.I. Akhmetgaliev, F.M.Gafarov, F.B.Sitdikova. Solving the Problem of Sentiment Analysis Using Neural Network Models. // International Journal of Pharmaceutical Research, Jan - March 2020, Vol 12, Issue 1, pp. 850-855. ISSN-0975-2366	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=221637&p_lang=2	ru_RU
dc.description.abstract	International Journal of Pharmaceutical Research	ru_RU
dc.description.abstract	The article considers methods that create a vector representation of words in the n-dimensional vector space in order to solving the problem of sentiment analysis based on neural network models of natural language processing . The methods are based on «Word2Vec«, «GloVe«, «FastText« technology. Approaches are used in the tasks of classification, sentiment analysis, typo correction, recommendation systems. We present the results of classifications comparison in the problem of sentiment analysis of a multilayer perceptron, a convolutional and recurrent neural network, decision trees (random forest), support vector machine (SVM), naive Bayes classifier (NB), and k-nearest neighbors (K-NN). The results of the classification are presented for three data sets: Twitter messages, reviews of various goods and services, Russian-language news.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	sentiment analysis	ru_RU
dc.subject	Word2Vec	ru_RU
dc.subject	GloVe	ru_RU
dc.subject	FastText	ru_RU
dc.subject	vector word representation	ru_RU
dc.subject	recurrent neural networks	ru_RU
dc.subject	convolutional neural networks	ru_RU
dc.title	Solving the Problem of Sentiment Analysis Using Neural Network Models.	ru_RU
dc.type	Articles in international journals and collections	ru_RU