Explaining sentiment analysis results on social media texts through visualization
Open accessAbstract
Today, Artificial Intelligence is achieving prodigious real-time performance, thanks to growing computational data and power capacities. However, there is little knowledge about what system results convey; thus, they are at risk of being susceptible to bias, and with the roots of Artificial Intelligence ("AI") in almost every territory, even a minuscule bias can result in excessive damage. Efforts towards making AI interpretable have been made to address fairness, accountability, and transparency concerns. This paper proposes two unique methods to understand the system's decisions aided by visualizing the results. For this study, interpretability has been implemented on Natural Language Processing-based sentiment analysis using data from various social media sites like Twitter, Facebook, and Reddit. With Valence Aware Dictionary for Sentiment Reasoning ("VADER"), heatmaps are generated, which account for visual justification of the result, increasing comprehensibility. Furthermore, Locally Interpretable Model-Agnostic Explanations ("LIME") have been used to provide in-depth insight into the predictions. It has been found experimentally that the proposed system can surpass several contemporary systems designed to attempt interpretability.
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Black box nature of AI model architecture restricts generalizability and interoperability, which may cause incorrect diagnosis.
“In addition, the black box nature of AI-model architecture restricts the generalizability and interoperability of the model which may cause incorrect diagnosis in the prediction [55,104].”
From Artificial intelligence bias in medical system designs: a systematic review (2023) · cites this paper as [55]
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Cited by
Sorted by citations
Artificial intelligence bias in medical system designs: a systematic review
Kumar, A., Aelgani, V., Vohra, R., Gupta, S. K., Bhagawati, M., Paul, S., Saba, L., Suri, N., Khanna, N. N., Laird, J. R., et al. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16029-x · Cited by 65 · see citing works
Self-citation
- Authors
- Jain, R., Kumar, A., Nayyar, A., Dewan, K., Garg, R., Raman, S., & Ganguly, S.
- Affiliations
- Bennett University (IN)
- Bharati Vidyapeeth's College of Engineering, Delhi (IN)
- Duy Tan University (VN)
- Corresponding author
- Nayyar, Anand
- Venue
- Multimedia Tools and Applications
- Published
- 2023-02-02
- Volume
- 82
- Issue
- 15
- Pages
- 22613-22629
- ISSN
- 1380-7501 (ISSN-L)
- 1573-7721
- PMC
- PMC9892668
- OpenAlex
- W4319020189
- Cited by
- 68
- References
- 54
- Citations by year
- 2026: 12 · 2025: 18 · 2024: 27 · 2023: 11
- FWCI
- 6.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Multimedia Tools and Applications (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- sentiment analysis, interpretability, explainable AI, social media text, VADER, lime, heatmaps, model-agnostic explanation, transparency and fairness, algorithmic bias
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