Political Opinion Mining from Twitter

Political Opinion Mining from Twitter

Yashvardhan Sharma (Computer Science Department, Birla Institute of Technology and Science, Pilani, India), Ekansh Mittal (Computer Science, Department, Birla Institute of Technology and Science, Pilani, India) and Mayank Garg (Electrical and Electronics Department, Birla Institute of Technology and Science, Pilani, India)
DOI: 10.4018/IJISSS.2016100104
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Abstract

Twitter is one of the most popular micro-blogging platform for people to express their political views in and around the elections. Hence during pre-elections twitter becomes a rich resource of data to understand the changing tenor of political leaders with time. During this time, when views, opinions and judgments are shared so prolifically through online media, tools which can provide the crux of this content are paramount. In this paper the authors have developed one such sentiment analysis tool to analyze the changing political views of persons with time. Using the tool they classify the tweets as positive, negative or neutral and studying it over time the authors successfully estimate the mood of the person. The authors have also developed a specialized phonetic dictionary to provide better approximation for most commonly used slangs and abbreviations.
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2. Literature Review

(Pang et al., 2008) presented a wide-ranging and detailed review of traditional automatic sentiment detection techniques, including many sub-components. In general, sentiment detection techniques can roughly be divided into lexicon-based methods and machine-learning methods described by (E.Boiy et al., 2009). Lexicon-based methods discussed by (M Taboada et al., 2010). rely on a sentiment lexicon, a collection of known and precompiled sentiment terms. Machine learning approaches make use of syntactic and/or linguistic features and hybrid approaches are very common, with sentiment lexicons playing a key role in the majority of these methods. However, such approaches are often inflexible regarding the ambiguity of sentiment terms. (Maynard et al., 2011) discussed opinion mining from micro posts and the challenges on NLP system, and suggested evolved techniques to handle them.

In our paper an application for political opinion mining is developed using GATE a concept given by (H. Cunningham et al., 2012). and is a freely available toolkit for language processing. ANNIE by (H. Cunningham, 2011). is the default named entity recognition system and a part of GATE in used for Tokenization, Gazetteer, Sentence Splitting and POS tagging which are essential for further processing and analysis. Before analysis the tweets are pre- processed to remove non opinionated and irrelevant tweets and Java Suggester, an open source java program, is used for spell checking. A Phonetic Dictionary of most commonly used 5000 words was developed using Metaphone algorithm given by (Philips Lawrence, 1990). to identify words using their pronunciation. For example the word ‘gud’ for ‘good’ or ‘awsum’ for ‘awesome’ are commonly used in micro posts and basic spell checkers fail to correctly identify them, hence for this purpose the dictionary is formulated.

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