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Approach to Sentiment Analysis and Business Communication on Social Media

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Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

Social media sites, namely Facebook and Twitter, have gained popularity in current times. These sites generate huge data that expresses thoughts and opinions of its users on issues. In this context, private individuals become the sources of information through online sharing of opinions and thoughts. These thoughts and opinions can be extracted to show patterns on sentiments of its users. Sentiment analysis, also referred as opinion mining, studies the “opinions, attitudes and emotions” of peoples. Sentiment analysis is a text classification problem, and identifying and extracting the right sentiment from huge data on social media sites are challenge. In this chapter, we presented different approaches to sentiment analysis on social media sites and social media business communication.

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Author information

Authors and Affiliations

  1. ICT and Society Research Group, Durban University of Technology, Durban, South Africa

    Israel Edem Agbehadji

  2. International Association for Impact Assessment (Member), Environmental Learning Research Centre, Rhodes University, Grahamstown, South Africa

    Abosede Ijabadeniyi

Authors
  1. Israel Edem Agbehadji
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  2. Abosede Ijabadeniyi
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Correspondence to Israel Edem Agbehadji .

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Editors and Affiliations

  1. University of Macau, Taipa, China

    Simon James Fong

  2. Durban University of Technology, Durban, South Africa

    Richard C. Millham

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Agbehadji, I.E., Ijabadeniyi, A. (2021). Approach to Sentiment Analysis and Business Communication on Social Media. In: Fong, S., Millham, R. (eds) Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-6695-0_9

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