Improving the BiLSTM model performance for tweet sentiment analysis

Research Article
Open access

Improving the BiLSTM model performance for tweet sentiment analysis

Jiaheng Dong 1*
  • 1 The University of Melbourne    
  • *corresponding author Jiahengd@student.unimelb.edu.au
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230415
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Twitter is a microblogging website where users can publish brief entries known as tweets. These tweets can occasionally reveal the users' attitudes and feelings. This paper analyses three emoticon processing methods with the BiLSTM model to discover the efficiencies of different methods in helping deep learning models classify tweet sentiments. Firstly, the simply removing method, the replacing with description text method, and the replacing with predefined sentiment method are established. Then the BiLSTM model is used to train and test with different methods on the Sentiment140 dataset. The performances of all models are evaluated by accuracy, F1 score, precision score, and recall score. The experimental results show that the replacing with predefined sentiments method provides the highest accuracy which is 0.84. The simply removing method also produces the testing accuracy as 0.84, but it performs worse in the last epoch, the training and validation accuracy, and the training and validation loss. The replacing with description text method produces the worst accuracy which is 0.83. It indicates that predefining the most possible sentiments of the popular emoticons has a reliable efficiency in optimizing the performance of deep learning models when the tweets with emoticons take a small proportion.

Keywords:

emoji, emoticon, sentiment analysis, Twitter.

Dong,J. (2023). Improving the BiLSTM model performance for tweet sentiment analysis. Applied and Computational Engineering,6,1106-1117.
Export citation

References

[1]. Mandloi L and Patel R 2020 Twitter Sentiments Analysis Using Machine Learninig Methods. 2020 International Conference for Emerging Technology (INCET). 1-5.

[2]. Goyal G 2022 Twitter Sentiment Analysis|Implement Twitter Sentiment Analysis Model. Analytics Vidhya.

[3]. R Inc 2022 Sentiment Analysis Challenges: Everything You Need to Know. Repustate.com.

[4]. Rish I 2001 An empirical study of the naive Bayes classifier. In IJCAI 2001 workshop on empirical methods in artificial intelligence. 3 22 41-46.

[5]. Cortes C and Vapnik V 1995 Support-vector networks. Machine learning. 20 (3) 273-97.

[6]. Cramer J S 2002 The origins of logistic regression (Technical report). Tinbergen Institute. 119 167–78.

[7]. Williams R J, Hinton G E and Rumelhart D E. 1986 Learning representations by back-propagating errors. Nature. 323 (6088) 533–6.

[8]. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L and Polosukhin I 2017 Attention Is All You Need.

[9]. Go A, Bhayani R and Huang L 2009 Twitter sentiment classification using distant supervision. CS224N Project Report. Stanford. 1 12.

[10]. 2022 The Most Used Emoji on Twitter in Every Country. Crossword-solver.io.

[11]. Mahto P 2021 Text Preprocessing: How to handle Emoji ‘😄’ & Emoticon ‘ :-) ’?. Medium.

[12]. Francesco B, Francesco R and Horacio S 2016 What does this emoji mean? A vector space skip-gram model for twitter emojis. In Proceedings of the Tenth International Conference on Language Resources and Evaluation LREC 2016. Portoroz. Slovenia.

[13]. Ayvaz S and Shiha M 2017 The Effects of Emoji in Sentiment Analysis. International Journal of Computer and Electrical Engineering. 9 1 360-9.

[14]. Althobaiti M 2022 BERT-based Approach to Arabic Hate Speech and Offensive Language Detection in Twitter: Exploiting Emojis and Sentiment Analysis. International Journal of Advanced Computer Science and Applications. 13 5.

[15]. Singh A, Blanco E and Jin W 2019 Incorporating Emoji Descriptions Improves Tweet Classification. Proceedings of the 2019 Conference of the North.

[16]. 2022 Papers with Code - BiLSTM Explained. Paperswithcode.com.

[17]. Karamitsos I, Afzulpurkar A and Trafalis T 2020 Malware Detection for Forensic Memory Using Deep Recurrent Neural Networks. Journal of Information Security. 11 02 103-120.

[18]. Mikolov T, et al. 2013 Efficient Estimation of Word Representations in Vector Space.

[19]. Mikolov T 2013 Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems.

Cite this article

Dong,J. (2023). Improving the BiLSTM model performance for tweet sentiment analysis. Applied and Computational Engineering,6,1106-1117.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Mandloi L and Patel R 2020 Twitter Sentiments Analysis Using Machine Learninig Methods. 2020 International Conference for Emerging Technology (INCET). 1-5.

[2]. Goyal G 2022 Twitter Sentiment Analysis|Implement Twitter Sentiment Analysis Model. Analytics Vidhya.

[3]. R Inc 2022 Sentiment Analysis Challenges: Everything You Need to Know. Repustate.com.

[4]. Rish I 2001 An empirical study of the naive Bayes classifier. In IJCAI 2001 workshop on empirical methods in artificial intelligence. 3 22 41-46.

[5]. Cortes C and Vapnik V 1995 Support-vector networks. Machine learning. 20 (3) 273-97.

[6]. Cramer J S 2002 The origins of logistic regression (Technical report). Tinbergen Institute. 119 167–78.

[7]. Williams R J, Hinton G E and Rumelhart D E. 1986 Learning representations by back-propagating errors. Nature. 323 (6088) 533–6.

[8]. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L and Polosukhin I 2017 Attention Is All You Need.

[9]. Go A, Bhayani R and Huang L 2009 Twitter sentiment classification using distant supervision. CS224N Project Report. Stanford. 1 12.

[10]. 2022 The Most Used Emoji on Twitter in Every Country. Crossword-solver.io.

[11]. Mahto P 2021 Text Preprocessing: How to handle Emoji ‘😄’ & Emoticon ‘ :-) ’?. Medium.

[12]. Francesco B, Francesco R and Horacio S 2016 What does this emoji mean? A vector space skip-gram model for twitter emojis. In Proceedings of the Tenth International Conference on Language Resources and Evaluation LREC 2016. Portoroz. Slovenia.

[13]. Ayvaz S and Shiha M 2017 The Effects of Emoji in Sentiment Analysis. International Journal of Computer and Electrical Engineering. 9 1 360-9.

[14]. Althobaiti M 2022 BERT-based Approach to Arabic Hate Speech and Offensive Language Detection in Twitter: Exploiting Emojis and Sentiment Analysis. International Journal of Advanced Computer Science and Applications. 13 5.

[15]. Singh A, Blanco E and Jin W 2019 Incorporating Emoji Descriptions Improves Tweet Classification. Proceedings of the 2019 Conference of the North.

[16]. 2022 Papers with Code - BiLSTM Explained. Paperswithcode.com.

[17]. Karamitsos I, Afzulpurkar A and Trafalis T 2020 Malware Detection for Forensic Memory Using Deep Recurrent Neural Networks. Journal of Information Security. 11 02 103-120.

[18]. Mikolov T, et al. 2013 Efficient Estimation of Word Representations in Vector Space.

[19]. Mikolov T 2013 Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.