Journal article
Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs
EPJ data science, v 6(1), pp 21-28
01 Oct 2017
Abstract
The emergence and global adoption of social media has rendered possible the real-time estimation of population-scale sentiment, an extraordinary capacity which has profound implications for our understanding of human behavior. Given the growing assortment of sentiment-measuring instruments, it is imperative to understand which aspects of sentiment dictionaries contribute to both their classification accuracy and their ability to provide richer understanding of texts. Here, we perform detailed, quantitative tests and qualitative assessments of 6 dictionary-based methods applied to 4 different corpora, and briefly examine a further 20 methods. We show that while inappropriate for sentences, dictionary-based methods are generally robust in their classification accuracy for longer texts. Most importantly they can aid understanding of texts with reliable and meaningful word shift graphs if (1) the dictionary covers a sufficiently large portion of a given text's lexicon when weighted by word usage frequency; and (2) words are scored on a continuous scale.
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Details
- Title
- Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs
- Creators
- Andrew J. Reagan - University of VermontChristopher M. Danforth - University of VermontBrian Tivnan - Mitre (United States)Jake Ryland Williams - Drexel UniversityPeter Sheridan Dodds - University of Vermont
- Publication Details
- EPJ data science, v 6(1), pp 21-28
- Publisher
- Springer Nature
- Number of pages
- 21
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000414227900001
- Scopus ID
- 2-s2.0-85032565890
- Other Identifier
- 991021806684604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Mathematics, Interdisciplinary Applications
- Social Sciences, Mathematical Methods