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Article

Shubhadeep Mukherjee and Pradip Kumar Bala

The purpose of this paper is to study sarcasm in online text – specifically on twitter – to better understand customer opinions about social issues, products, services…

Abstract

Purpose

The purpose of this paper is to study sarcasm in online text – specifically on twitter – to better understand customer opinions about social issues, products, services, etc. This can be immensely helpful in reducing incorrect classification of consumer sentiment toward issues, products and services.

Design/methodology/approach

In this study, 5,000 tweets were downloaded and analyzed. Relevant features were extracted and supervised learning algorithms were applied to identify the best differentiating features between a sarcastic and non-sarcastic sentence.

Findings

The results using two different classification algorithms, namely, Naïve Bayes and maximum entropy show that function words and content words together are most effective in identifying sarcasm in tweets. The most differentiating features between a sarcastic and a non-sarcastic tweet were identified.

Practical implications

Understanding the use of sarcasm in tweets let companies do better sentiment analysis and product recommendations for users. This could help businesses attract new customers and retain the old ones resulting in better customer management.

Originality/value

This paper uses novel features to identify sarcasm in online text which is one of the most challenging problems in natural language processing. To the authors’ knowledge, this is the first study on sarcasm detection from a customer management perspective.

Details

Industrial Management & Data Systems, vol. 117 no. 6
Type: Research Article
ISSN: 0263-5577

Keywords

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Article

Rahul Kumar, Shubhadeep Mukherjee, Bipul Kumar and Pradip Kumar Bala

Colossal information is available in cyberspace from a variety of sources such as blogs, reviews, posts and feedback. The mentioned sources have helped in improving…

Abstract

Purpose

Colossal information is available in cyberspace from a variety of sources such as blogs, reviews, posts and feedback. The mentioned sources have helped in improving various business processes from product development to stock market development. This paper aims to transform this wealth of information in the online medium to economic wealth. Earlier approaches to investment decision-making are dominated by the analyst's recommendations. However, their credibility has been questioned for herding behavior, conflict of interest and favoring underwriter's firms. This study assumes that members of the online crowd who have been reliable, profitable and knowledgeable in the recent past will continue to be so soon.

Design/methodology/approach

The authors identify credible members as experts using multi-criteria decision-making tools. In this work, an alternative actionable investment strategy is proposed and demonstrated through a mock-up. The experimental prototype is divided into two phases: expert selection and investment.

Findings

The created portfolio is comparable and even profitable than several major global stock indices.

Practical implications

This work aims to benefit individual investors, investment managers and market onlookers.

Originality/value

This paper takes into account factors: the accuracy and trustworthiness of the sources of stock market recommendations. Earlier work in the area has focused solely intelligence of the analyst for the stock recommendation. To the best of the authors’ knowledge, this is the first time that the combined intelligence of the virtual investment communities has been considered to make stock market recommendations.

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Book part

Madhvi Sethi, Pooja Gupta, Shubhadeep Mukherjee and Siddhi Agrawal

Behavioral finance literature has long claimed that internet stock message boards can move markets. In this chapter, the authors study more than 2,000 internet board…

Abstract

Behavioral finance literature has long claimed that internet stock message boards can move markets. In this chapter, the authors study more than 2,000 internet board messages posted across finance message boards in India (Chittorgarh, etc.) for 110 companies that went for initial public offering (IPO) in the last one year. This study has multi-fold objectives. First, the authors try to identify the factors which lead to a discussion on an IPO stock in the message board. Second, the authors identify the factors which differentiate a widely discussed stock from the less discussed one. Next, the authors apply advanced machine learning technique to identify the topics which are discussed in the message board through automatic topic modeling. The methodology used includes a logistic regression model for identifying firm characteristics which leads to a probability of getting stakeholders’ attention and hence more discussion. The authors also use advanced topic modeling techniques to identify topics of discussion on the message boards through machine learning. The authors find that larger sized firms, younger firms, firms with low leverage, and non-manufacturing firms get discussed more and the topics of discussion relate to their financial statements, trading strategies, stock behavior, and performance.

Details

Financial Issues in Emerging Economies: Special Issue Including Selected Papers from II International Conference on Economics and Finance, 2019, Bengaluru, India
Type: Book
ISBN: 978-1-83867-960-6

Keywords

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Abstract

Details

Financial Issues in Emerging Economies: Special Issue Including Selected Papers from II International Conference on Economics and Finance, 2019, Bengaluru, India
Type: Book
ISBN: 978-1-83867-960-6

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Article

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

Abstract

Purpose

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

Design/methodology/approach

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Findings

Growing doubts about the reliability of professional analysts is making many investors hesitant to use the conventional approach to stock market investment. They are instead becoming increasingly attracted to an alternative strategy based on recommendations offered from members of virtual communities. Objective criteria are used to identify experts within such domains who have the potential to generate results comparable with major global indices.

Originality/value

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Details

Strategic Direction, vol. 37 no. 2
Type: Research Article
ISSN: 0258-0543

Keywords

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