Comparison of the scope of negation in online news articles

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.