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Political Sentiment Mining: A New Age Intelligence Tool for Business Strategy Formulation

Political Sentiment Mining: A New Age Intelligence Tool for Business Strategy Formulation

Nishikant Bele, Prabin Kumar Panigrahi, Shashi Kant Srivastava
Copyright: © 2017 |Volume: 8 |Issue: 1 |Pages: 16
ISSN: 1947-3591|EISSN: 1947-3605|EISBN13: 9781522514091|DOI: 10.4018/IJBIR.2017010104
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MLA

Bele, Nishikant, et al. "Political Sentiment Mining: A New Age Intelligence Tool for Business Strategy Formulation." IJBIR vol.8, no.1 2017: pp.55-70. http://doi.org/10.4018/IJBIR.2017010104

APA

Bele, N., Panigrahi, P. K., & Srivastava, S. K. (2017). Political Sentiment Mining: A New Age Intelligence Tool for Business Strategy Formulation. International Journal of Business Intelligence Research (IJBIR), 8(1), 55-70. http://doi.org/10.4018/IJBIR.2017010104

Chicago

Bele, Nishikant, Prabin Kumar Panigrahi, and Shashi Kant Srivastava. "Political Sentiment Mining: A New Age Intelligence Tool for Business Strategy Formulation," International Journal of Business Intelligence Research (IJBIR) 8, no.1: 55-70. http://doi.org/10.4018/IJBIR.2017010104

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Abstract

Investigations on sentiment mining are mostly ensued in the English language. Due to the characteristics of the Indian languages tools and techniques used for sentiment mining in the English language cannot be applied directly to text in Hindi languages. The objective of this paper is to extract the political sentiment at the document-level from Hindi blogs. The authors could not find any literature about extracting sentiments at the document-level from Hindi blogs. They extracted opinion about one of India's very famous leaders who was a prominent face in the national election of 2014. They prepared the datasets from Hindi blogs reviews. They purposed the lexicon and machine learning technique to classify the sentiment. Their purposed method used four steps: (1) Crawling and preprocessing the blog reviews; (2) Extracting reviews relevant to the query using the Vector Space Model (VSM); (3) Identifying sentiment at the document level using the Lexicon method, and (4) Measuring the result using the Machine learning technique. Their experimental result demonstrates the effectiveness of our algorithms.

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