Comparative Analysis of Word2Vec and GloVe with LSTM for Sentiment Analysis: Accuracy and Loss Evaluation on Twitter Data

Joshua Chibuike Sopuru; Adah Alubo; Princess Chinemerem Iloh; Oluwaseun Augustine Lottu

Authors

Joshua Chibuike Sopuru Girne American University
Adah Alubo University of Hull UK, Data Science and artificial Intelligence
Princess Chinemerem Iloh Teesside university UK
Oluwaseun Augustine Lottu Stanmore College IT and Engineering Lecturer, UK

Keywords:

Sentimental analysis , Word2Vec , Artificial intelligence , GloVe , LSTM , Twitter dataset

Abstract

Artificial Intelligence (AI) is witnessing an increase in textual data from diverse sources such as social media, online reviews, and blogs. This textual data, rich in sentiments and emotions, has become a valuable asset for understanding public opinion and societal trends. Conventional sentiment analysis methods, relying on lexicon-based approaches and machine learning models, faced challenges in handling linguistic subtleties and contextual nuances. The advent of deep learning, particularly Long Short-Term Memory (LSTM) architecture, has revolutionized sentiment analysis by enabling automated pattern extraction from raw textual data. This article investigates the efficacy of Word2Vec and GloVe models in combination with LSTM for sentiment analysis using a Twitter dataset.

References

Acosta, J., Lamaute, N., Luo, M., Finkelstein, E., & Andreea, C. (2017). Sentiment analysis of twitter messages using word2vec. Proceedings of student-faculty research day, CSIS, Pace University, 7, 1-7.

Annett, M., & Kondrak, G. (2008). A comparison of sentiment analysis techniques: Polarizing movie blogs. In Advances in Artificial Intelligence: 21st Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2008 Windsor, Canada, May 28-30, 2008 Proceedings 21 (pp. 25-35). Springer Berlin Heidelberg.

Araque, O., Corcuera-Platas, I., Sánchez-Rada, J. F., & Iglesias, C. A. (2017). Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Systems with Applications, 77, 236-246.

Chassagnon, G., Vakalopolou, M., Paragios, N., & Revel, M. P. (2020). Deep learning: definition and perspectives for thoracic imaging. European radiology, 30, 2021-2030.

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2021). Deep learning--based text classification: a comprehensive review. ACM computing surveys (CSUR), 54(3), 1-40.

Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).

Sachin, S., Tripathi, A., Mahajan, N., Aggarwal, S., & Nagrath, P. (2020). Sentiment analysis using gated recurrent neural networks. SN Computer Science, 1, 1-13.

Sopuru, J. C., & Akkaya, M. (2019). Guide for Modelling a Network Flow-Based Detection System for Malware Categorization: A Review of Related Literature. Applying Methods of Scientific Inquiry Into Intelligence, Security, and Counterterrorism, 150-178.

Singh, P. K., & Paul, S. (2021). Deep learning approach for negation handling in sentiment analysis. IEEE Access, 9, 102579-102592.

Topal, M. O., Bas, A., & van Heerden, I. (2021). Exploring transformers in natural language generation: Gpt, bert, and xlnet. arXiv preprint arXiv:2102.08036.

Vijayvergia, A., & Kumar, K. (2021). Selective shallow models strength integration for emotion detection using GloVe and LSTM. Multimedia Tools and Applications, 80(18), 28349-28363.

Xiao, L., Wang, G., & Zuo, Y. (2018, December). Research on patent text classification based on word2vec and LSTM. In 2018 11th International Symposium on Computational Intelligence and Design (ISCID) (Vol. 1, pp. 71-74). IEEE.