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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

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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。
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报道来源 [2]

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

    揭示公众舆论：一项使用LSTM和传统模型的情感分析研究

    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 ·

    揭示公众舆论：一项关于使用LSTM和传统模型的情感分析研究

    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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