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Evaluation of deep learning models for sentiment analysis

Evaluation of deep learning models for sentiment analysis Sentiment analysis is a task that belongs to natural language processing and it is highly used in texts extracted from social networks. This task consists of assigning the labels or classes: positive, negative or neutral to the text. However, analyzing a piece of text extracted from social networks to determine if it represents a positive or negative sentiment is a difficult task, because social media texts contain slangs, typographical errors and cultural context. The shortcomings of traditional frequency based feature extraction models such as bag of words or TF-IDF affect the accuracy of sentiment classification. To improve the precision in the sentiment classification task, it is possible to use natural language modelling methods that are able to learn contextual information from words. In this work, word embedding such as Word2Vec, GloVe and Doc2VecC with different dimensions are used. The resulting word vectors will be used to train recurring neural networks such as LSTM, BiLSTM, GRU and BiGRU, to improve sentiment classification. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Intelligent & Fuzzy Systems IOS Press

Evaluation of deep learning models for sentiment analysis

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Publisher
IOS Press
Copyright
Copyright © 2022 © 2022 – IOS Press. All rights reserved
ISSN
1064-1246
eISSN
1875-8967
DOI
10.3233/jifs-211909
Publisher site
See Article on Publisher Site

Abstract

Sentiment analysis is a task that belongs to natural language processing and it is highly used in texts extracted from social networks. This task consists of assigning the labels or classes: positive, negative or neutral to the text. However, analyzing a piece of text extracted from social networks to determine if it represents a positive or negative sentiment is a difficult task, because social media texts contain slangs, typographical errors and cultural context. The shortcomings of traditional frequency based feature extraction models such as bag of words or TF-IDF affect the accuracy of sentiment classification. To improve the precision in the sentiment classification task, it is possible to use natural language modelling methods that are able to learn contextual information from words. In this work, word embedding such as Word2Vec, GloVe and Doc2VecC with different dimensions are used. The resulting word vectors will be used to train recurring neural networks such as LSTM, BiLSTM, GRU and BiGRU, to improve sentiment classification.

Journal

Journal of Intelligent & Fuzzy Systems – IOS Press

Published: Nov 11, 2022

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