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Solving the Problem of Sentiment Analysis Using Neural Network Models
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| Author:
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AYNUR AKHMETGALIEV, FAIL MUBARAKOVICH GAFAROV, FARIDA BIZYANOVNA SITDIKOVA
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| Abstract:
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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.
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| Keyword:
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sentiment analysis, Word2Vec, GloVe, FastText, vector word representation, recurrent neural networks, convolutional neural networks.
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| EOI:
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| DOI:
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https://doi.org/10.31838/ijpr/2020.12.01.162
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| Download:
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Request For Article
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