КФУ. Карточка публикации. Solving the Problem of Sentiment Analysis Using Neural Network Models.

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SOLVING THE PROBLEM OF SENTIMENT ANALYSIS USING NEURAL NETWORK MODELS.

Форма представления

Статьи в зарубежных журналах и сборниках

Год публикации

2020

Язык

русский

Авторы, сотрудники КФУ

Гафаров Фаиль Мубаракович, автор

Ситдикова Фарида Бизяновна, автор

Авторы, студенты КФУ

Ахметгалиев Айнур Исламович, автор

Библиографическое описание на языке оригинала

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

Аннотация

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.

Ключевые слова

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

Название журнала

International Journal of Pharmaceutical Research

URL

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

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https://repository.kpfu.ru/?p_id=221637

Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Гафаров Фаиль Мубаракович	ru_RU
dc.contributor.author	Ситдикова Фарида Бизяновна	ru_RU
dc.contributor.author	Ахметгалиев Айнур Исламович	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/?p_id=221637	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	Статьи в зарубежных журналах и сборниках	ru_RU