解密 iPhone 14 上的公众情绪:基于 BERT 的 Twitter 话语分析

Tianxuan Sun, Tianji Sun, Longhao Tan
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引用次数: 0

摘要

本研究通过情感分析模型深入了解 Twitter 用户对 iPhone 14 的情感。由于认识到传统情感分析工具的局限性,研究采用了以双向理解文本上下文而著称的 BERT 模型。最初,研究使用一般模型观察到了普遍的负面情绪。然而,在使用 BERT 对方法进行改进后,观察到的情感表现更加平衡。BERT 模型的结果凸显了 Twitter 上对 iPhone 14 的正面和负面反应,情感得分出现了明显的峰值。这项研究强调了利用 BERT 等高级模型完成需要细致入微理解的任务的重要性,为技术领域的利益相关者提供了对公众情绪的全面洞察。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deciphering Public Sentiment on iPhone 14: A BERT-Based Analysis of Twitter Discourse
The study delves into understanding the sentiment of Twitter users towards the iPhone 14 by employing sentiment analysis models. Recognizing the limitations of traditional sentiment analysis tools, the research utilizes the BERT model, known for its bidirectional understanding of textual context. Initially, the study observed a prevailing negative sentiment using general models. However, after refining the approach using BERT, a more balanced representation of sentiments was observed. The BERT model's results highlighted both positive and negative reactions towards the iPhone 14 on Twitter, with distinct peaks in sentiment scores. The research underscores the importance of leveraging advanced models like BERT for tasks requiring nuanced understanding, providing stakeholders in the technology domain with comprehensive insights into public sentiment.
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