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Proceeding Paper

Social Implications of Technological Advancements in Sentiment Analysis: A Literature Review on Potential and Consequences over the Next 20 Years †

by
Daryanto
1,2,*,
Ika Safitri Windiarti
1,3 and
Bagus Setya Rintyarna
4
1
Department of Information Technology, Universiti Muhammadiyah Malaysia (UMAM), Padang Besar 021011, Perlis, Malaysia
2
Department of Informatics, Universitas Muhammadiyah Jember, Jember 68121, Jawa Timur, Indonesia
3
Departement of Computer Science, Universitas Muhammadiyah Palangkaraya (UMPR), Palangkaraya 73111, Kalimantan Tengah, Indonesia
4
Department of Electrical Engineering, Universitas Muhammadiyah Jember, Jember 68121, Jawa Timur, Indonesia
*
Author to whom correspondence should be addressed.
†
Presented at the 8th Mechanical Engineering, Science and Technology International Conference, Padang Besar, Perlis, Malaysia, 11–12 December 2024.
Eng. Proc. 2025, 84(1), 49; https://doi.org/10.3390/engproc2025084049
Published: 10 February 2025
(This article belongs to the Proceedings of The 8th Mechanical Engineering, Science and Technology International Conference)
Versions Notes

Abstract

This study uses a literature review method to examine the social impact of technological advancements in sentiment analysis and its potential and consequences over the next 20 years. Key findings indicate that sentiment analysis technology significantly benefits customer service, business decision-making, and real-time reputation monitoring sectors. It enables more responsive policy design by understanding public emotions in political and social contexts. However, data privacy, misinformation, and diminished critical thinking persist. This study contributes to the existing literature by comprehensively analyzing ethical and regulatory needs and identifying integration opportunities with IoT, big data, and AI to maximize benefits while minimizing risks. Practically, it offers actionable policy recommendations for leveraging sentiment analysis responsibly to promote societal well-being and foster sustainable development.

1. Introduction

The rapid advancement of information and communication technology over the past two decades has brought about significant transformations in various aspects of human life. Among these transformative technologies, sentiment analysis stands out. Also known as opinion mining, sentiment analysis is a powerful technique that identifies, extracts, and analyzes feelings or opinions expressed in text. This technology, powered by natural language processing (NLP), machine learning, and data mining, can analyze data from diverse sources such as social media, blogs, product reviews, and news articles, opening up a world of possibilities [1]. Sentiment analysis has great potential in various fields. For example, companies can leverage this technology to improve customer service and better understand customer needs and complaints. This allows them to enhance product and service quality, increasing customer satisfaction and loyalty [2].
Additionally, sentiment analysis can provide valuable insights into market trends and consumer preferences, which companies can use to make more accurate decisions in marketing strategies, product development, and risk management [3]. Companies can also use sentiment analysis to monitor how their brand is discussed on social media and other online platforms, enabling them to quickly respond to negative issues and improve their company image [4].
In the political and social context, sentiment analysis can be used to understand public emotions and opinions on government policies, political campaigns, or specific social issues, helping design policies more responsive to public needs [5]. However, the advancement of sentiment analysis technology also brings various social impacts that must be considered. Massively collecting and analyzing sentiment data can raise concerns regarding individual privacy. Personal data collected without consent can be misused, making it essential to develop strict regulations to protect user privacy [6].
Moreover, this technology can be used to manipulate public opinion, for instance, through disseminating misinformation or propaganda on social media, which can disrupt social and political stability and erode public trust in available information [7]. Dependence on technology can also diminish individuals’ ability to think critically and independently, leading to the homogenization of opinions and reducing the diversity of perspectives [8].
In terms of mental health, monitoring and analyzing sentiment on social media can reveal negative tendencies such as cyberbullying or social pressure, which can aid in designing more effective interventions to improve public mental health [9]. Future challenges include the development of increasingly sophisticated technology with the ability to understand more complex nuances and contexts. Ethics and regulation are also crucial aspects, where the development of sentiment analysis technology must be accompanied by the development of clear ethical and regulatory frameworks to ensure responsible use.
However, it is not just about regulations. Digital literacy also needs to be enhanced so that the public can better understand the potential and risks of this technology and actively participate in shaping its use [10]. In the next 20 years, sentiment analysis technology advancements will significantly impact various sectors. However, it is important to note that sentiment analysis has limitations. For instance, it may struggle with sarcasm or irony, and it may not always accurately reflect the true sentiment of a text. Despite these limitations, the global sentiment analysis market is projected to grow from USD 2.25 billion in 2020 to USD 5.46 billion by 2027, with a compound annual growth rate (CAGR) of 13.2% [11]. When used appropriately, this technology can improve societal well-being and facilitate better decision-making.

2. Research Method

This study investigates the social impact of technological advancements in sentiment analysis over the next 20 years. It aims to identify the benefits and applications of sentiment analysis technology across various sectors and potential negative social impacts such as data privacy issues, public opinion manipulation, and mental health concerns. The research also intends to formulate policy recommendations and best practices to address these negative impacts and maximize the benefits of the technology. Data for the study are gathered from various academic and non-academic sources, including scholarly journal articles, books, industry reports, conference proceedings, and policy documents. Keywords such as “sentiment analysis”, “social impact”, “technological advancement”, “privacy”, “public opinion manipulation”, and “mental health” are used for the literature search. The selection criteria for the sources include relevance, quality, publication date, and document type, focusing on including only analytical and empirical documents from reputable sources published within the last ten years, alongside the seminal literature. A rigorous content analysis approach is employed to extract essential information from each source, categorize it by central themes, and synthesize information from various sources to comprehend the social impact of sentiment analysis technology. Data triangulation is conducted to ensure the validity of the findings by comparing results from multiple sources, and peer review is sought from fellow researchers or experts in the field. The research findings are compiled into a report that includes an introduction, research methodology, main findings, discussion, policy recommendations, and a conclusion to provide a comprehensive overview of the social impact of sentiment analysis technology. This research methodology aims to equip the audience with the necessary information to make informed decisions and develop appropriate policies and strategies for responsibly utilizing sentiment analysis technology.

3. Results

Based on the literature review conducted, several key findings related to the social impact of technological advancements in sentiment analysis were identified. The findings are summarized in Table 1.

4. Discussion

One of the main benefits of sentiment analysis technology is improving customer service and satisfaction. Companies can use this technology to understand customer needs and complaints better, ultimately enhancing product and service quality [1,2,3]. Additionally, sentiment analysis helps companies make more accurate business decisions by providing insights into market trends and consumer preferences [3,4]. This technology also allows companies to monitor their brand and reputation across various online platforms, enabling them to address negative issues and improve their corporate image promptly [4,5]. In the political and social context, sentiment analysis can be used to understand public emotions towards government policies and specific social issues, aiding in designing policies more responsive to public needs [6,7]. On the other hand, technological advancements in sentiment analysis raise concerns about user data privacy. The massive collection of data without consent can lead to the misuse of personal data, making it crucial to develop strict regulations to protect user privacy [8,9]. This technology can also manipulate public opinion by disseminating misinformation, potentially disrupting social and political stability and eroding public trust in available information [10,11]. Additionally, reliance on this technology can diminish individuals’ ability to think critically and lead to the homogenization of opinions, reducing the diversity of perspectives [12,13,14]. In terms of mental health, monitoring and analyzing sentiment on social media can reveal negative tendencies such as cyberbullying and social pressure, helping design more effective interventions to improve public mental health [15,16]. The development of more advanced sentiment analysis technology presents both challenges and opportunities. Ongoing research aims to improve the accuracy and efficiency of the algorithms used [17,18]. Moreover, developing clear ethical and regulatory frameworks is essential to ensure the responsible use of this technology [19,20]. Integration with other technologies, such as the Internet of Things (IoT), big data, and artificial intelligence (AI), can open new opportunities for innovation and broader applications across various sectors [21,22]. Enhancing digital literacy is also a significant challenge to ensure that the public better understands the potential and risks of this technology [20,22]. The advancement of technology in sentiment analysis has significant positive and negative impacts. With the right approach and the development of adequate regulations, the benefits of this technology can be maximized while minimizing its negative consequences. This study guides developing appropriate policies and strategies to utilize sentiment analysis technology responsibly.

5. Conclusions

Through a literature review, this study examined the social impact of technological advancements in sentiment analysis and its potential and consequences over the next 20 years. Key findings include the significant benefits of sentiment analysis technology across various sectors. In business, it enhances customer service, aids in accurate decision-making, and enables real-time reputation monitoring. Politically and socially, it helps understand public emotions towards policies, facilitating more responsive policy design. However, negative impacts must be considered, such as concerns about user data privacy, manipulation of public opinion through misinformation, and a decline in critical thinking, alongside mental health issues related to cyberbullying and social pressure. To address these challenges and maximize the benefits, several recommendations are proposed: 1. Policymakers should establish and enforce clear ethical and regulatory frameworks to ensure responsible data collection and usage. 2. Organizations implementing sentiment analysis should prioritize transparency and adopt privacy-preserving technologies. 3. Public awareness and digital literacy programs should be enhanced to equip individuals with the knowledge to engage responsibly with sentiment analysis tools and their outputs. For future research, it is essential to explore the development of more nuanced sentiment analysis algorithms that can handle complex linguistic elements like sarcasm and cultural context. Additionally, longitudinal studies assessing the long-term societal impacts of sentiment analysis, especially in underrepresented regions, will provide deeper insights into its broader implications. Integrating sentiment analysis with emerging technologies like IoT and AI also presents innovation opportunities, enabling broader and more impactful applications.

Author Contributions

D. contributed to conceptualization, methodology, data collection, and analysis. I.S.W. supervised the research and provided guidance throughout the drafting process. B.S.R. contributed to the research process and also to writing, review, and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Dissertation Grant from Universitas Muhammadiyah Jember.

Institutional Review Board Statement

Ethical review and approval were waived for this study as it did not involve human or animal subjects, in accordance with institutional and national regulations.

Informed Consent Statement

Not applicable. This study did not involve human participants.

Data Availability Statement

This review is based on previously published data available in public databases, including SpringerLink, nlp.stanford.edu, Sheffield Hallam University Research Archive, Cambridge University Press, MDPI, and others. The articles referenced in this study can be accessed through these sources and are listed in the reference section.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Mitra, A.; Mohanty, S. Sentiment Analysis Using Machine Learning Approaches; Springer: Singapore, 2020; Volume 2. [Google Scholar]
  2. Liu, B.; Zhang, L. A survey of opinion mining and sentiment analysis. In Mining Text Data; Springer: Singapore, 2012; pp. 415–463. ISBN 978-1-4614-3223-4. [Google Scholar]
  3. Pang, B.; Lee, L. Opinion Mining and Sentiment Analysis. Found. Trends Inf. Retr. 2008, 2, 148. [Google Scholar] [CrossRef]
  4. Cambria, E.; Das, D.; Bandyopadhyay, S.; Feraco, A. Affective Computing and Sentiment Analysis. In A Practical Guide to Sentiment Analysis. Socio-Affective Computing; Springer: Cham, Switzerland, 2017; pp. 1–10. [Google Scholar] [CrossRef]
  5. Wiebe, J.; Wilson, T.; Cardie, C. Annotating expressions of opinions and emotions in language. Lang. Resour. Eval. 2005, 39, 165–210. [Google Scholar] [CrossRef]
  6. Schutze, H.; Manning, C.D.; Raghavan, P. Introduction to Information Retrieval; Cambridge University Press: Cambridge, UK, 2008. [Google Scholar]
  7. Baldwin, J. Analysing Social Media Data Using Sentiment Analysis in Relation to Public Order; Sheffield Hallam University (United Kingdom): Sheffield, UK, 2021. [Google Scholar]
  8. Mejova, Y.; Weber, I.; Macy, M.W. Twitter: A Digital Socioscope; Cambridge University Press: Cambridge, UK, 2015. [Google Scholar]
  9. Burnap, P.; Rana, O.F.; Avis, N.; Williams, M.; Housley, W.; Edwards, A.; Morgan, J.; Sloan, L. Detecting tension in online communities with computational Twitter analysis. Technol. Forecast. Soc. Chang. 2015, 95, 96–108. [Google Scholar] [CrossRef]
  10. Kouloumpis, E.; Wilson, T.; Moore, J. Twitter sentiment analysis: The good the bad and the omg! In Proceedings of the International AAAI Conference on Web and Social Media, Barcelona, Spain, 17–21 July 2011; pp. 538–541. [Google Scholar]
  11. Xu, Q.; Chang, V.; Hsu, C.-H. Event study and principal component analysis based on sentiment analysis–a combined methodology to study the stock market with an empirical study. Inf. Syst. Front. 2020, 22, 1021–1037. [Google Scholar] [CrossRef]
  12. Rintyarna, B.S.; Kuswanto, H.; Sarno, R.; Rachmaningsih, E.K.; Rachman, F.H.; Suharso, W.; Cahyanto, T. Modelling Service Quality of Internet Service Providers during COVID-19: The Customer Perspective Based on Twitter Dataset. Informatics 2022, 9, 11. [Google Scholar] [CrossRef]
  13. Feldman, R. Techniques and applications for sentiment analysis. Commun. ACM 2013, 56, 82–89. [Google Scholar] [CrossRef]
  14. Tsytsarau, M.; Palpanas, T. Survey on mining subjective data on the web. Data Min. Knowl. Discov. 2012, 24, 478–514. [Google Scholar] [CrossRef]
  15. Pak, A.; Paroubek, P. Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of the LREc, Valletta, Malta, 17–23 May 2010; pp. 1320–1326. [Google Scholar]
  16. Makri, K.; Papadas, K.; Schlegelmilch, B.B. Global social networking sites and global identity: A three-country study. J. Bus. Res. 2021, 130, 482–492. [Google Scholar] [CrossRef]
  17. Tumasjan, A.; Sprenger, T.; Sandner, P.; Welpe, I. Predicting elections with twitter: What 140 characters reveal about political sentiment. In Proceedings of the International AAAI Conference on Web and Social Media, Washington, DC, USA, 23–26 May 2010; pp. 178–185. [Google Scholar]
  18. Ortigosa, A.; Martin, J.M.; Carro, R.M. Sentiment analysis in Facebook and its application to e-learning. Comput. Human Behav. 2014, 31, 527–541. [Google Scholar] [CrossRef]
  19. Hou, J.; Yang, X. Social media-based sleeping beauties: Defining, identifying and features. J. Informetr. 2020, 14, 101012. [Google Scholar] [CrossRef]
  20. Golder, S.A.; Macy, M.W. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 2011, 333, 1878–1881. [Google Scholar] [CrossRef] [PubMed]
  21. Thelwall, M. Sentiment analysis and time series with Twitter. Twitter Soc. 2014, 89, 83–95. [Google Scholar]
  22. Melville, P.; Gryc, W.; Lawrence, R.D. Sentiment analysis of blogs by combining lexical knowledge with text classification. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, 28 June–1 July 2009; pp. 1275–1284. [Google Scholar]
Table 1. Several key findings related to the social impact.
Table 1. Several key findings related to the social impact.
AspectKey FindingsReferences
Benefits of Technology• Improved customer service and satisfaction[1,2,3]
• More accurate business decision-making[3,4,12]
• Brand and reputation monitoring[4,5]
• Understanding public emotions towards political and social issues[6,7]
Negative Impacts• Concerns regarding user data privacy[8,9]
• Manipulation of public opinion through misinformation[13,14]
• Decline in critical thinking and homogenization of opinions[10,15]
• Mental health issues related to cyberbullying and social pressure[11,16]
Challenges and Opportunities• Development of more advanced technology[17,18]
• Development of clear ethical and regulatory frameworks[19,20]
• Integration with other technologies such as IoT, big data, and AI[19,21]
• Enhancing digital literacy to understand the potential and risks of this technology[20,22]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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MDPI and ACS Style

Daryanto; Windiarti, I.S.; Rintyarna, B.S. Social Implications of Technological Advancements in Sentiment Analysis: A Literature Review on Potential and Consequences over the Next 20 Years. Eng. Proc. 2025, 84, 49. https://doi.org/10.3390/engproc2025084049

AMA Style

Daryanto, Windiarti IS, Rintyarna BS. Social Implications of Technological Advancements in Sentiment Analysis: A Literature Review on Potential and Consequences over the Next 20 Years. Engineering Proceedings. 2025; 84(1):49. https://doi.org/10.3390/engproc2025084049

Chicago/Turabian Style

Daryanto, Ika Safitri Windiarti, and Bagus Setya Rintyarna. 2025. "Social Implications of Technological Advancements in Sentiment Analysis: A Literature Review on Potential and Consequences over the Next 20 Years" Engineering Proceedings 84, no. 1: 49. https://doi.org/10.3390/engproc2025084049

APA Style

Daryanto, Windiarti, I. S., & Rintyarna, B. S. (2025). Social Implications of Technological Advancements in Sentiment Analysis: A Literature Review on Potential and Consequences over the Next 20 Years. Engineering Proceedings, 84(1), 49. https://doi.org/10.3390/engproc2025084049

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