Sentence-Level Sentiment Classification A Comparative Study Between Deep Learning Models

Sentiment classification provides a means of analysing the subjective information in the text and subsequently extracting the opinion. Sentiment analysis is the method by which people extract information from their opinions, judgments and emotions about entities. In this paper we propose a comparati...

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Bibliographic Details
Published in:Journal of ICT Standardization 2022, Vol.10 (2), p.339
Main Authors: Mifrah, Sara, Benlahmar, El Habib
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Classification
Comparative studies
Data mining
Datasets
Deep learning
Graphics processing units
Language
Linguistics
Machine learning
Microprocessors
Natural language processing
Neural networks
Optimization techniques
Sentiment analysis
Support vector machines
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Summary:Sentiment classification provides a means of analysing the subjective information in the text and subsequently extracting the opinion. Sentiment analysis is the method by which people extract information from their opinions, judgments and emotions about entities. In this paper we propose a comparative study between the most deep learning models used in the field of sentiment analysis; L-NFS (Linguistique Neuro Fuzzy System), GRU (Gated Recurrent Unit), BiGRU (Bidirectional Gated Recurrent Unit), LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory) and BERT(Bidirectional Encoder Representation from Transformers), we used for this study a large Corpus contain 1.6 Million tweets, as devices we train our models with GPU (graphics processing unit) processor. As result we obtain the best Accuracy and F1-Score respectively 87.36% and 0.87 for the BERT Model.
ISSN:2245-800X
2246-0853
DOI:10.13052/jicts2245-800X.10213