Abstract
Negation control for sentiment analysis is essential and effective decision support system. Negation control include identification of negation cues, scope of negation and their influence within it. Negation can either shift or change the polarity score of opinionated word. This paper present a framework for feature fusion of text feature extraction, negation cue and scope detection technique for enhancing the performance of recent sen-timent classifier for negation control. Explore text feature POS, BOW and HT with negation cue and scope detection techniques for classification technique over social media data set. This paper has included the evaluation of sentiment classification (Support vector machine, Navies Bayes, Linear Regression and Random Forest) and Nine feature fusion over presented prepossessing framework. This paper yield interesting result about collective response of feature fusion for negation scope detection and clas-sification technique. Feature Fusion vector significantly increase the polarity classification accuracy of sentiment classification technique. POS with Grammatical dependency tree can detect negation with better accuracy as compared to other feature fusion.
Highlights
Sentiment analysis (SA) is the computational analysis of the opinion, attitudes, emotions of speaker/writer towards some topic and identification of non- trivial, subjective information from text repository
In this paper we have summarized the effect of negation cues over sentiment analysis and introduced a comparative analysis of recent text feature extraction, negation cues and scope detection technique
This paper present a four tier framework for feature fusion of text feature extraction (POS, BOW and HT) and negation scope detection technique
Summary
Sentiment analysis (SA) is the computational analysis of the opinion, attitudes, emotions of speaker/writer towards some topic and identification of non- trivial, subjective information from text repository. In this paper we have summarized the effect of negation cues over sentiment analysis and introduced a comparative analysis of recent text feature extraction, negation cues and scope detection technique. This paper present a framework securitizing and preprocessed social media data set and formulate the supervised classification technique with feature fusion for negative sentiment analysis. The rest of the paper is organized as follows: Section 2 presents over view of Negative sentiment analysis; Section 3 covers related work on negation handle mechanism for sentiment analysis and polarity detection over social media data set.
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