利用新型混合机器学习技术检测新闻标题中的讽刺性文字

Neha Singh
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引用次数: 0

摘要

情感分析系统最大的问题之一就是讽刺。使用含蓄、间接的语言表达观点是其复杂性所在。讽刺可以通过多种方式表达,例如标题、对话或书名。即使对人类来说,识别讽刺也是很困难的,因为它所传达的感情与文字所表达的字面意思截然相反。有几种不同的讽刺检测模型。为了识别幽默的新闻标题,本文评估了矢量化算法和几种机器学习模型。通过实验,将推荐的使用词袋和 TF-IDF 特征矢量化模型的混合技术与其他机器学习方法进行了比较。与现有的策略相比,实验证明,建议的混合技术采用词袋向量化模型,具有更高的准确性和 F1 分数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques
One of the biggest problems with sentiment analysis systems is sarcasm. The use of implicit, indirect language to express opinions is what gives it its complexity. Sarcasm can be represented in a number of ways, such as in headings, conversations, or book titles. Even for a human, recognizing sarcasm can be difficult because it conveys feelings that are diametrically contrary to the literal meaning expressed in the text. There are several different models for sarcasm detection. To identify humorous news headlines, this article assessed vectorization algorithms and several machine learning models. The recommended hybrid technique using the bag-of-words and TF-IDF feature vectorization models is compared experimentally to other machine learning approaches. In comparison to existing strategies, experiments demonstrate that the proposed hybrid technique with the bag-of-word vectorization model offers greater accuracy and F1-score results.
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