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English(EN) Unveiling Public Opinion: A Study of Sentiment Analysis Using LSTM and Traditional Models

LSTM模型在Twitter感情分析中优于传统方法 · 跟踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员在arXiv上发表了一项研究，比较了各种机器学习和深度学习模型在Twitter数据感情分析中的有效性。该研究评估了逻辑回归、随机森林、朴素贝叶斯、梯度提升和长短期记忆（LSTM）网络。LSTM模型表现出卓越的性能，训练准确率为90.98%，测试准确率为80.00%，微平均ROC-AUC得分为0.92，在捕捉上下文和顺序文本细微差别方面优于传统的机器学习方法。 AI

影响 强调了LSTM模型在分析社交媒体公众舆论方面的卓越性能，可能改进趋势预测。

排序理由 该集群包含一篇在arXiv上发表的研究论文，详细介绍了感情分析模型的研究。

在 arXiv cs.CL 阅读 →

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LSTM模型在Twitter感情分析中优于传统方法 · 跟踪2个来源

报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Atiq Ur Rehman ·

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

    arXiv:2607.07772v1 Announce Type: new 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 applica…

  2. arXiv cs.CL TIER_1 English(EN) · Atiq Ur Rehman ·

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

    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…

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