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

被引：0
作者：
Mifrah S. [1 ]
Benlahmar E.H. [1 ]
机构：
[1] Laboratory of Information Processing and Modelling, Hassan II University of Casablanca, Faculty of Sciences Ben M’sik, Casablanca
来源：
Journal of ICT Standardization | 2022年 / 10卷 / 02期
关键词：
BERT; BiGRU; BiLSTM; deep learning; GRU; L-NFS; LSTM; sentence level; Sentiment classification; transformer;
D O I：
10.13052/jicts2245-800X.10213
中图分类号：
学科分类号：
摘要：
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. © 2022 River Publishers
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页码：339 / 352
页数：13
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