Abstract
Automatic detection of linguistic negation in free text is a demanding need for many text processing applications including Sentiment Analysis. Our system uses online news archives from two different resources namely NDTV and The Hindu. While dealing with news articles, we performed three subtasks namely identifying the target; separation of good and bad news content from the good and bad sentiment expressed on the target and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. In this paper, our main focus was on evaluating and comparing three sentiment analysis methods (two machine learning based and one lexical based) and also identifying 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). Among the sentiment methods the best F-measure was SVM with the values 0.688 and 0.657 for BJP and UPA respectively. On the other hand, the F measures for RoS, FWL and DA 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.
Highlights
Access to popular communication platforms have given a way for the public to generate emotions, opinions, sentiments, evaluations, appraisals and attitudes towards entities such as products, services, organizations, individuals, issues, events, topics, and their attributes
The goal of this work is to evaluate and compare three sentiment analysis methods and to identify the scope of negation in news articles for two political parties namely BJP and United Progressive Alliance (UPA) by using three existing methodologies namely Rest of the Sentence (RoS), Fixed Window Length (FWL) and Dependency Analysis (DA) with respect to sentiment expressed in online news archives
In order to understand the advantages, disadvantages, and limitations of the various sentiment analysis methods and analyze the choice of words from news articles in winning a particular party, we present comparison results among them which are as shown below
Summary
Access to popular communication platforms have given a way for the public to generate emotions, opinions, sentiments, evaluations, appraisals and attitudes towards entities such as products, services, organizations, individuals, issues, events, topics, and their attributes. The volume of such information created every day is massive. With the effect of such networks on a daily basis, we start recoiling our valuable decisions and actions with certain predefined notions created by others through their reviews It is both interesting and challenging to see what and how the trends in such OSNs change from time to time. This gives an edge to Text Mining and explores the area of Sentiment Analysis (Opinion Mining) which is designated as an automatic processing of texts to detect opinions
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More From: International Journal of Advanced Research in Artificial Intelligence
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