Simplifying Sentiment Analysis on Social Media: A Step-by-Step Approach
Article
Article Title | Simplifying Sentiment Analysis on Social Media: A Step-by-Step Approach |
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ERA Journal ID | 19788 |
Article Category | Article |
Authors | Chau, Xuan Truong Du, Nguyen, Thanh Toan, Jo, Jun, Quach, Sara, Ngo, Liem Viet, Pham, Hien and Thaichon, Park |
Journal Title | Australasian Marketing Journal |
Journal Citation | 32 (4), pp. 367-380 |
Number of Pages | 14 |
Year | 2024 |
Publisher | SAGE Publications Ltd |
Place of Publication | United Kingdom |
ISSN | 1441-3582 |
1839-3349 | |
Digital Object Identifier (DOI) | https://doi.org/10.1177/14413582231217126 |
Web Address (URL) | https://journals.sagepub.com/doi/10.1177/14413582231217126 |
Abstract | This tutorial presents a systematic guide to performing sentiment analysis on social media data, designed to be accessible to researchers and marketers with varying levels of data science expertise. We prioritise open science by providing comprehensive resources, including self-collected data, source code and guidelines, facilitating result reproduction. For marketing and business researchers without programming experience, this tutorial offers a robust resource for conducting sentiment analysis. Experienced data scientists can use it as a reference for evaluating cutting-edge approaches and streamlining the sentiment analysis process. Our work stands out in its unique perspective on the challenges and opportunities of sentiment analysis within the social media data domain. We delve into the potential of sentiment analysis for social media marketing, offering practical guidance and best practices for enhancing brand reputation and customer engagement. Notably, this tutorial advances beyond previous studies by comprehensively comparing a wide range of sentiment analysis methods, including state-of-the-art transfer learning approaches, filling a critical gap in the existing literature. Our commitment to transparency underscores our contribution, as we provide all necessary resources for result reproducibility. We make our resources available at the following address: https://tinyurl.com/SentimentTutorial. |
Keywords | big data analytics; social media data; marketing decisions; sentiment analy |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 350612. Social marketing |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Griffith University |
HUTECH University of Technology, Vietnam | |
University of New South Wales | |
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia | |
University of Southern Queensland |
https://research.usq.edu.au/item/zv09q/simplifying-sentiment-analysis-on-social-media-a-step-by-step-approach
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