How Social Media Influencers Govern Sentiment Territory

How Social Media Influencers Govern Sentiment Territory

Vala Ali Rohani (Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia), Shahid Shayaa (Department of Data Analytics and Research, Berkshire Media, Petaling Jaya, Malaysia) and Ghazaleh Babanejaddehaki (Faculty of Computer Science and Information Technology, University Putra Malaysia, Kuala Lumpur, Malaysia)
Copyright: © 2017 |Pages: 12
DOI: 10.4018/ijaec.2017010104
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Abstract

In present research, the authors examined how social media influencers affect the overall sentiment of a topic. To this end, they utilized supervised machine learning approach to develop SentiRobo for measuring the sentiment score of social media content. In the next stage, they studied social media datasets with 375,141 records in the education domain to investigate the correlation between social media topics and top authors' sentiment. The Pearson correlation test results revealed that top one percent of social media authors are enough to significantly influence the whole sentiment of each topic.
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‘Sentiment’ is defined by Wiebe (2004) as the expression of somebody’s opinions, sentiments, emotions, evaluations, and beliefs. It is believed that opinion mining systems are capable to process a set of search results for a given item, generate a list of product attributes, and aggregate opinions about each of them (Dave et al., 2003). Here, opinion mining is considered as extracting and analyzing the people feelings about given products from different aspects. In another similar research work, Hue and Liu focuses on feature-based opinion mining (2004). In 2005, Kim and Hovey defined opinion with four features of Topic, Holder, Claim, and Sentiment, in which the Holder presents his Claim about the Topic with a Sentiment like positive or negative.

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