Sentiment Analysis of Public Opinion Using LSTM and Traditional Models in 2026

On July 8, 2026, a study titled Unveiling Public Opinion: A Study of Sentiment Analysis Using LSTM and Traditional Models by Atiq Ur Rehman was submitted to arXiv. This research explores the significance of sentiment analysis in understanding public opinion through social media platforms like Twitter, particularly in the context of real-time discussions on various issues.

Understanding Sentiment Analysis in Social Media

In today's digital landscape, social media serves as a crucial platform for individuals to express their thoughts and feelings. With the rapid increase in user-generated content, sentiment analysis has emerged as a vital tool in natural language processing (NLP). This method categorizes tweets into positive, negative, or neutral sentiments, allowing researchers and businesses to gain insights into public opinion.

The study emphasizes that sentiment analysis not only evaluates individual tweets but also analyzes large datasets related to specific events or topics. By leveraging machine learning models, researchers can enhance their understanding of public sentiment and forecast trends more accurately.

Machine Learning Approaches in Sentiment Analysis

This research assesses the effectiveness of various machine learning and deep learning techniques for sentiment classification. The models evaluated include logistic regression, random forest, naïve bayes, gradient boosting, and LSTM networks. The findings indicate that the LSTM approach significantly outperforms traditional methods in capturing the nuances of contextual and sequential textual information.

According to the study, the LSTM model achieved a training accuracy of 90.98%, a testing accuracy of 80.00%, and a micro-average ROC-AUC score of 0.92. These metrics demonstrate the model's superior ability to interpret sentiment compared to other algorithms.

Implementation and Future Implications

The research utilized a Kaggle Twitter dataset that underwent preprocessing steps, including tokenization, lemmatization, and stopword elimination. The results from this study underscore the importance of selecting appropriate models for sentiment analysis, particularly in the evolving landscape of social media data.

As sentiment analysis continues to play a critical role in understanding public opinion, the implications of this research could influence various sectors, including marketing, politics, and social research. By adopting advanced techniques like LSTM, organizations can better gauge public sentiment and respond to emerging trends.