Unveiling Public Opinion: A Study of Sentiment Analysis Using LSTM and Traditional Models
Quick Answer
This study evaluates sentiment analysis on Twitter using various models, highlighting LSTM's superior performance with a training accuracy of 90.98% and testing accuracy of 80.00%.
Quick Take
This study evaluates sentiment analysis on Twitter using various models, highlighting LSTM's superior performance with a training accuracy of 90.98% and testing accuracy of 80.00%. The research demonstrates that LSTM outperforms traditional methods like logistic regression and random forest in capturing contextual nuances.
Key Points
- LSTM achieved a training accuracy of 90.98% and testing accuracy of 80.00%.
- The study compares LSTM with logistic regression, random forest, and naive Bayes.
- LSTM outperformed traditional models in sentiment classification tasks.
- Sentiment analysis helps interpret public opinion and forecast trends.
- The research utilized a preprocessed Kaggle Twitter dataset for evaluation.
Paper Resources
📖 Reader Mode
~3 min read
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CL
See more →Quantifying Prior Dominance in Systems
The study introduces the Normalized Context Utilization (NCU) metric to evaluate Retrieval-Augmented Generation (RAG) systems, revealing that Small Language Models (SLMs) outperform larger models in factual extraction. The findings indicate that traditional scaling laws yield diminishing returns, with a commercial API frequently failing against adversarial evidence due to systemic confidence collapse.