Automatic Classification of Social Media Messaging Using Multi-Dimensional Sentiment Analysis and Crowdsourcing

10 Pages Posted: 5 Apr 2013

See all articles by Radu Machedon

Radu Machedon

University of Maryland - College Park

William Rand

North Carolina State University

Yogesh V. Joshi

University of Maryland - Department of Marketing

Date Written: February 15, 2013

Abstract

In order to study the use of Twitter, it would be useful to be able to easily classify large volumes of messaging being used by Twitter users besides just in the traditional dimensions of positive and negative sentiment. For instance, in the space of social media marketing, it would be useful to be able to automatically identify social media messages into the classic framework of informative, persuasive and transformative advertising. In this paper we propose a general method for using crowdsourced training data labels for supervised machine learning, in order to automatically classify social media messaging with multidimensional sentiment analysis. We proceed with our methodology to create classifiers in the context of a firm’s messaging, but we hypothesize that its applications are more general than this context. Our results show that the proposed method works for the relatively objective attribute of informativeness, but is not as useful for more subjective attributes such as persuasiveness and transformativeness. We finish by discussing recommendations for future work in this area.

Keywords: Social Media, Machine Learning, Sentiment Analysis, Informativity, Transformativity, Persuasiveness, Advertising

JEL Classification: M37, C80

Suggested Citation

Machedon, Radu and Rand, William and Joshi, Yogesh V., Automatic Classification of Social Media Messaging Using Multi-Dimensional Sentiment Analysis and Crowdsourcing (February 15, 2013). Available at SSRN: https://ssrn.com/abstract=2244353 or http://dx.doi.org/10.2139/ssrn.2244353

Radu Machedon

University of Maryland - College Park ( email )

College Park, MD 20742
United States

William Rand (Contact Author)

North Carolina State University ( email )

Raleigh, NC 27695
United States

Yogesh V. Joshi

University of Maryland - Department of Marketing ( email )

College Park, MD 20742
United States

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