Journal article
The wisdom of the lexicon crowds: leveraging on decades of lexicon-based sentiment analysis for improved results
Journal of big data, v 12(1), 129
23 May 2025
Abstract
The “wisdom of the crowd” (WoC) refers to the notion that collective human knowledge is capable of outperforming even individual expert knowledge. This study investigates the application of this phenomenon to lexicon-based sentiment analysis of text data. Lexicons are frequently used to classify the sentiment of text data, particularly in the absence of sentiment class label information. We propose leveraging some of the most popular, publicly-available lexicons created in the last half century to improve sentiment analysis performance. Specifically, this research argues that the collective information provided by the thirteen lexicons included in the crowd constitutes a WoC situation that can more accurately predict the sentiment in the majority of example cases when compared to individual lexicons, lexicon ensembles, and machine learning methods. Thirteen popular sentiment-labeled text datasets, comprised of different types of text data and covering a variety of domains, are used to test this research proposition. We show that the WoC sentiment analysis achieves greater performance than individual lexicons, which are considered to be ‘experts’, and a lexicon ensemble approach. In comparing our novel approach to sentiment analysis against popular machine learning approaches, the proposed WoC method achieves superior results in the majority of examples. By overcoming many of the limitations of other approaches with high accuracy, the WoC method can provide organizations with real-time, reliable, and accurate sentiment analysis.
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Details
- Title
- The wisdom of the lexicon crowds: leveraging on decades of lexicon-based sentiment analysis for improved results
- Creators
- Chelsey H. Hill - Montclair State UniversityJorge E. Fresneda - New Jersey Institute of TechnologyMurugan Anandarajan - Drexel University
- Publication Details
- Journal of big data, v 12(1), 129
- Publisher
- Springer
- Number of pages
- 30
- Grant note
- Martin Tuchman School of Management
The authors want to thank the Martin Tuchman School of Management for its financial assistance.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:001493480300002
- Scopus ID
- 2-s2.0-105006651744
- Other Identifier
- 991022053877804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Theory & Methods