Optimizing LSTM Models for Tweet Sentiment Analysis: A Hyperparameter Study

Yuxuan Wu ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2025, vol. 8, issue 1, 128-135

Abstract: This research investigates the effectiveness of using LSTM models for sentiment analysis of tweet data. By preprocessing the data and systematically optimizing the hyperparameters (e.g., number of LSTM units, dropout rate, batch size, and epoch), an LSTM model was constructed for tweet sentiment classification. In the test set, the model achieves 75% accuracy, verifying its feasibility in short text. Additionally, the experiments show that choosing the appropriate batch size and epoch number can improve the efficiency and stability of the model, which provides a reference for future research on sentiment analysis.

Keywords: LSTM; Sentiment Analysis; Twitter Data; Deep Learning; Hyperparameter Tuning; Natural Language Processing (NLP); Neural Networks (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://newjaigs.com/index.php/JAIGS/article/view/354 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:das:njaigs:v:8:y:2025:i:1:p:128-135:id:354

Access Statistics for this article

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 is currently edited by Justyna Żywiołek

More articles in Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 from Open Knowledge
Bibliographic data for series maintained by Open Knowledge ().