Unveiling Public Opinion: A Study of Sentiment Analysis Using LSTM and Traditional Models

揭示公众舆论：基于 LSTM 与传统模型的情感分析研究

Abstract: In this age of social media, sites like Twitter have become meeting places for people to share their views and feelings on a wide range of issues and current events as they unfold in real time. Sentiment analysis, a critical application of NLP, has become indispensable due to the massive influx of user-generated content, enabling the extraction of meaningful insights from the opinions and emotions expressed in textual data.

摘要： 在社交媒体时代，Twitter 等网站已成为人们实时分享对各类议题和时事看法与感受的聚集地。情感分析作为自然语言处理（NLP）的一项关键应用，因海量用户生成内容的涌入而变得不可或缺，它能够从文本数据所表达的观点和情绪中提取有价值的见解。

Sentiment analysis on Twitter employs sophisticated computational techniques to categorize tweets into positive, negative, or neutral sentiments. This method not only examines individual expressions but also analyzes vast databases related to specific subjects or events. By spotting these emotions, machine learning models help improve public opinion interpretation and trend forecasting.

Twitter 上的情感分析采用复杂的计算技术，将推文归类为正面、负面或中性情感。该方法不仅能考察个人表达，还能分析与特定主题或事件相关的大型数据库。通过识别这些情绪，机器学习模型有助于改善公众舆论的解读与趋势预测。

This paper examines the effectiveness of various machine learning and deep learning approaches. Designed for this use, the system evaluates logistic regression, random forest, naïve bayes, gradient boosting, and LSTM networks, among other algorithms applied in sentiment classification. This work identifies the optimal sentiment analysis model using a Kaggle Twitter dataset that has been preprocessed through tokenization, lemmatization, and stopword elimination.

本文探讨了多种机器学习和深度学习方法的有效性。该系统专为此目的设计，评估了逻辑回归、随机森林、朴素贝叶斯、梯度提升和 LSTM 网络等应用于情感分类的算法。本研究使用经过分词、词形还原和停用词过滤预处理的 Kaggle Twitter 数据集，确定了最优的情感分析模型。

Emphasizing the better performance of the LSTM approach, the model attained a training accuracy of 90.98%, a testing accuracy of 80.00%, and a micro-average ROC-AUC score of 0.92. These results show that the model outperforms conventional machine learning techniques in capturing contextual and sequential textual aspects.

研究强调了 LSTM 方法的优越性能，该模型达到了 90.98% 的训练准确率、80.00% 的测试准确率以及 0.92 的微平均 ROC-AUC 分数。这些结果表明，该模型在捕捉文本的上下文和序列特征方面优于传统的机器学习技术。

Publication Details:

Authors: Atiq Ur Rehman
Journal Reference: Proceedings of the 2025 IEEE International Conference on Computing, Communication and Data Engineering (C-CODE), 2025
DOI: 10.48550/arXiv.2607.07772

出版详情：

作者： Atiq Ur Rehman
期刊参考： 2025 年 IEEE 计算、通信与数据工程国际会议 (C-CODE) 会议录，2025
DOI： 10.48550/arXiv.2607.07772