Comparison of the scope of negation in online news articles

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2014-12-01 DOI:10.1109/ICCCT2.2014.7066744

S. Padmaja, S. Fatima, Sasidhar Bandu, B. Sowmya

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引用次数: 2

Abstract

Electronic detection of linguistic negation in free text is a challenging need for many text handling applications including sentiment analysis. Our system uses online news archives from two different resources namely NDTV and The Hindu to predict the scope of negation in the text. In this paper, our main target was on determining the scope of negation in news articles for two political parties namely BJP and UPA by using three existing methodologies. They were Rest of the Sentence (RoS), Fixed Window Length (FWL) and Dependency Analysis (DA). The F measures for each one of them were 0.58, 0.69 and 0.75 respectively. We observed that DA was performing better than the other two. Among 1675 sentences in the corpus, according to annotator I, 1,137 were positive and 538 were negative whereas according to annotator II, 1,130 were positive and 545 were negative. Further we also identified the score of each sentence and calculated the accuracy on the basis of average score of both the annotators. The scope of negation detection was limited to specific rather than implicit negations within single sentences.

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网络新闻文章中否定范围的比较

对于包括情感分析在内的许多文本处理应用来说，自由文本中语言否定的电子检测是一个具有挑战性的需求。我们的系统使用来自新德里电视台和印度教徒报两个不同资源的在线新闻档案来预测文本中的否定范围。在本文中，我们的主要目标是通过使用三种现有方法确定两个政党即人民党和团结进步联盟的新闻文章中的否定范围。它们分别是句子剩余(RoS)、固定窗口长度(FWL)和依赖分析(DA)。F值分别为0.58、0.69、0.75。我们观察到，DA的性能优于其他两个。在1675个语料库中，根据注释者1，有1137个是肯定句，538个是否定句，而根据注释者2，有1130个是肯定句，545个是否定句。此外，我们还确定了每个句子的分数，并根据两个注释者的平均分数计算准确率。否定检测的范围仅限于单句内的特定否定，而非隐含否定。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2021 RIVF International Conference on Computing and Communication Technologies (RIVF)

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