Explaining sentiment analysis results on social media texts through visualization
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 Intellig...
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| Published in | Multimedia tools and applications Vol. 82; no. 15; pp. 22613 - 22629 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.06.2023
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1380-7501 1573-7721 |
| DOI | 10.1007/s11042-023-14432-y |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Author | Jain, Rachna Nayyar, Anand Dewan, Kritika Ganguly, Sahil Garg, Rishika Raman, Shatakshi Kumar, Ashish |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36747895$$D View this record in MEDLINE/PubMed |
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| Keywords | Visualization VADER Interpretability Explainability LIME |
| Language | English |
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| SubjectTerms | Artificial intelligence Bias Computer Communication Networks Computer Science Data mining Data Structures and Information Theory Digital media Multimedia Information Systems Natural language processing Sentiment analysis Social networks Special Purpose and Application-Based Systems |
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| Title | Explaining sentiment analysis results on social media texts through visualization |
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