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Journal ArticleDOI

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

TL;DR: Investigating Document Level Opinion Mining, Hindi Blogs Reviews, Hindi Language, Information Search and Retrieval, Machine Learning Techniques, Natural Languages Processing, Opinion Mining.
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 langua...
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Book ChapterDOI
18 Feb 2022
TL;DR: In this article , the authors conducted a systematic literature review based on PRISMA methodology to identify the most frequently used lexicons in political sentiment analysis, their results, similarities, and differences.
Abstract: This chapter presented an analysis of the application of lexicon-based political sentiment analysis in social media. The aim is to identify the most frequently used lexicons in political sentiment analysis, their results, similarities, and differences. For this, the authors conducted a systematic literature review based on PRISMA methodology. Afinn, NRC, and SenticNet lexicons are tested and combined for data analysis from the 2020 U.S. presidential campaign. Findings show that political sentiment analysis is a new field studied for only 10 years. Political sentiment analysis could generate benefits in understanding problems such as political polarization, discourse analysis, politician influence, candidate profiling, and improving government-citizen interaction, among other problems in the public sphere, enhanced by the combination of lexicons and multimodal analysis. The authors conclude that polarity was one of the critical dimensions identified for finding variations in the behavior and polarity of sentiments. Limitations and future work also are presented.

4 citations

References
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Journal ArticleDOI
TL;DR: The authors provide an empirical example on how firms might improve reputation management through sensing social media, and analyze a dataset of 271,207 messages about an American Bank collected on Twitter.
Abstract: The business agility concept reflects an organization's need to develop sensing capabilities for being able to respond to changes in the business environment. Therefore, intelligent information systems are needed to support decision makers with accurate and timely information. Since corporate reputation is among the most valuable assets, organizations need efficient measuring techniques to manage it. Recently, due to the advent of social media new reputational challenges have emerged for firms, since such technologies significantly increase the risk for being associated with negative issues. Therefore, organizations should utilize there IT-systems for actively sensing social media content as a basis for a quick response to reputational threats. Accordingly, the authors provide an empirical example on how firms might improve reputation management through sensing social media. Specifically, the authors analyze a dataset of 271,207 messages about an American Bank collected on Twitter. For their empirical investigation, the applied automated sentiment analysis and manual content analysis.

24 citations

Book ChapterDOI
01 Jan 2014
TL;DR: This paper investigated the feasibility of combining purely linguistic indicators of political sentiment with non-linguistic evidence gained from concomitant social network analysis using a hybrid machine-learning and logic-based framework which operates along three distinct levels of analysis encompassing standard shallow document classification, deep linguistic multi-entity sentiment analysis and scoring and social network modeling.
Abstract: Automatic computational analysis of political texts poses major challenges for state-of-the-art Sentiment Analysis and Natural Language Processing tools. In this initial study, we investigate the feasibility of combining purely linguistic indicators of political sentiment with non-linguistic evidence gained from concomitant social network analysis. The analysis draws on a corpus of 2.8 million political blog posts by 16,741 bloggers. We focus on modeling blogosphere sentiment centered around Barack Obama during the 2008 U.S. presidential election, and describe a series of initial sentiment classification experiments on a data set of 700 crowd-sourced posts labeled for attitude with respect to Obama. Our approach employs a hybrid machine-learning and logic-based framework which operates along three distinct levels of analysis encompassing standard shallow document classification, deep linguistic multi-entity sentiment analysis and scoring and social network modeling. The initial results highlight the inherent complexity of the classification task and point towards the positive effects of learning features that exploit entity-level sentiment and social-network structure.

21 citations

Journal ArticleDOI
TL;DR: In this article, a text mining using centering resonance analysis (CRA) revealed that Indian firms see prevailing industry practices such as reverse logistics, product recycling, and/or improving supplier environmental performance as immaterial.

19 citations

Journal ArticleDOI
TL;DR: Problem noted in the use of DM in the field of BI is related to the fact that DM models are complex in order to be directly manipulated by business users, not including BI tools.
Abstract: Since Lunh first used the term Business Intelligence BI in 1958, major transformations happened in the field of information systems and technologies, especially in the area of decision support systems. BI systems are widely used in organizations and their importance is recognized. These systems present themselves as essential parts of a complete knowledge of business and an irreplaceable tool in the support to decision making. The dissemination of data mining DM tools is increasing in the BI field, as well as the acknowledgment of the relevance of its usage in enterprise BI systems. BI tools are friendly, iterative, and interactive, allowing business users an easy access. The user can manipulate directly data, having the ability to extract all the value contained into that business data. Problems noted in the use of DM in the field of BI is related to the fact that DM models are complex in order to be directly manipulated by business users, not including BI tools. The nonexistence of BI tools allowing business users the direct manipulation of DM models was identified as the problem. More of these issues, possible solutions and conclusions are presented in this article.

18 citations

Proceedings ArticleDOI
01 Dec 2011
TL;DR: It is evident that Bag of Words approach performs significantly better than Naïve Bayesian approach and can be extended further to sentiment classification, Question Answering, Text Summarization and also for customer reviews in Kannada Blogs.
Abstract: With the rapid growth of internet, huge amount of data is available online. The ability to draw useful information from this digital data is quite challenging. The task of exploring and extracting information from native languages available on line is very much a useful task. The work presented here focuses on sentence level classification in the Kannada language. The most popular approaches in text categorization like Naive Bayesian and Bag of Words (BOW) approaches are used in this work. It is evident that Bag of Words approach performs significantly better than Naive Bayesian approach. The objective of the work is to find how sentence level classification works for Kannada Language, as it can be extended further to sentiment classification, Question Answering, Text Summarization and also for customer reviews in Kannada Blogs, because most user's comments, queries, opinions etc are expressed using sentences, hence this sentence level Text Classification becomes a special task of Text Classification problem. The work though focuses on very basic approaches presently, can later be extended to other methods like SVM, KNN etc.

18 citations