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


  • 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


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


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.


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How to Cite

Sopuru, J. C., Alubo, A., Iloh, P. C., & Lottu, O. A. (2024). Comparative Analysis of Word2Vec and GloVe with LSTM for Sentiment Analysis: Accuracy and Loss Evaluation on Twitter Data. International Journal of Social Sciences and Scientific Studies, 3(6), 3458 - 3468. Retrieved from