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Studying social media sentiment using human validated analysis.

James Lappeman1, Robyn Clark2, Jordan Evans2

  • 1UCT Liberty Institute of Strategic Marketing, School of Management Studies, University of Cape Town, South Africa.

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|April 18, 2020
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Summary
This summary is machine-generated.

This study introduces a novel method for analyzing online sentiment using natural language processing (NLP) and human validation. It enables businesses to track sentiment shifts and conversation topics effectively.

Keywords:
Consumer sentimentNegative word-of-mouth (nWOM)Online firestormsSocial media

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Area of Science:

  • Social Science
  • Big Data Research
  • Computational Linguistics

Background:

  • Online sentiment measurement is an evolving field.
  • Existing methods lack a structured approach combining big data and human validation.
  • Tracking public opinion is crucial for businesses.

Purpose of the Study:

  • To develop and present a unique methodology for analyzing online sentiment.
  • To create net sentiment scores and categorize online conversation topics.
  • To apply this methodology to South Africa's retail banking sector over 12 months.

Main Methods:

  • Utilizing a combination of Natural Language Processing (NLP) and human validation techniques.
  • Implementing microsampling for manual validation of sentiment analysis (qualitative and quantitative).
  • Developing a sentiment measurement and mapping system.

Main Results:

  • Successfully measured online sentiment for major South African banks.
  • Demonstrated the ability to track sentiment shifts, including extreme 'firestorms'.
  • Identified and categorized key online conversation topics relevant to the banking industry.

Conclusions:

  • The proposed methodology offers a structured and accurate approach to online sentiment analysis.
  • Firms can leverage this method to monitor brand perception and customer feedback.
  • This combined NLP and human validation approach sets a new standard for sentiment research.