Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment Analysis

Diwali, Arwa; Saeedi, Kawther; Dashtipour, Kia; Gogate, Mandar; Wang, Zhaoxia; Hussain, Amir

doi:10.1109/taffc.2023.3296373

Cited by 6 publications

(2 citation statements)

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“…As a result, we opted to use the LIWC framework, 7 because it allows us to fully understand why a given post is considered to have (or not) negative emotion, while also being able to reliably capture such emotion (trade-off between prediction accuracy and explainability). On the other hand, this capability is often not provided by neural network-based models that could also be used for determining the sentiment of a post, as the model's interpretability often decreases with the model's predictive power [56].…”

Section: Restricting Entropy Computation To Negative Informationmentioning

confidence: 99%

Quantifying polarization in online political discourse

Muñoz,

Bellogín,

Barba-Rojas

et al. 2024

EPJ Data Sci.

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In an era of increasing political polarization, its analysis becomes crucial for the understanding of democratic dynamics. This paper presents a comprehensive research on measuring political polarization on X (Twitter) during election cycles in Spain, from 2011 to 2019. A wide comparative analysis is performed on algorithms used to identify and measure polarization or controversy on microblogging platforms. This analysis is specifically tailored towards publications made by official political party accounts during pre-campaign, campaign, election day, and the week post-election. Guided by the findings of this comparative evaluation, we propose a novel algorithm better suited to capture polarization in the context of political events, which is validated with real data. As a consequence, our research contributes a significant advancement in the field of political science, social network analysis, and overall computational social science, by providing a realistic method to capture polarization from online political discourse.

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Section: Restricting Entropy Computation To Negative Informationmentioning

confidence: 99%

Quantifying polarization in online political discourse

Muñoz,

Bellogín,

Barba-Rojas

et al. 2024

EPJ Data Sci.

View full text Add to dashboard Cite

show abstract

“…Therefore, sentiment analysis models based on deep learning have the advantages of automatic learning and feature extraction, high accuracy, and high flexibility. Deep learning models are particularly adept at capturing long-range dependencies from sequence data, which is crucial for understanding the context of text [8]. However, deep learning models typically require a large amount of annotated data for training, otherwise it may www.ijacsa.thesai.org lead to overfitting.…”

Section: Introductionmentioning

confidence: 99%

Design of Emotion Analysis Model IABC-Deep Learning-based for Vocal Performance

Zhu,

2024

IJACSA

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With the development of deep learning technology, and due to its potential in solving optimization problems with deep structures, deep learning technology is gradually being applied to sentiment analysis models. However, most existing deep learning-based sentiment analysis models have low accuracy issues. Therefore, this study focuses on the issue of emotional analysis in vocal performance. Firstly, based on vocal performance experts and user experience, classify the emotions expressed in vocal performance works to identify the emotional representations of music. On this basis, in order to improve the accuracy of emotion analysis models for deep learning based vocal performance, an improved artificial bee colony algorithm (IABC) was developed to optimize deep neural networks (DNN). Finally, the effectiveness of the proposed deep neural network based on improved artificial bee colony (IABC-DNN) was verified through a training set consisting of 150 vocal performance works and a testing set consisting of 30 vocal performance works. The results indicate that the accuracy of the sentiment analysis model for vocal performance based on IABC-DNN can reach 98%.

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Cognitive-Inspired Deep Learning Models for Aspect-Based Sentiment Analysis: A Retrospective Overview and Bibliometric Analysis

Chen,

Xie,

Qin

et al. 2024

Cogn Comput

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As cognitive-inspired computation approaches, deep neural networks or deep learning (DL) models have played important roles in allowing machines to reach human-like performances in various complex cognitive tasks such as cognitive computation and sentiment analysis. This paper offers a thorough examination of the rapidly developing topic of DL-assisted aspect-based sentiment analysis (DL-ABSA), focusing on its increasing importance and implications for practice and research advancement. Leveraging bibliometric indicators, social network analysis, and topic modeling techniques, the study investigates four research questions: publication and citation trends, scientific collaborations, major themes and topics, and prospective research directions. The analysis reveals significant growth in DL-ABSA research output and impact, with notable contributions from diverse publication sources, institutions, and countries/regions. Collaborative networks between countries/regions, particularly between the USA and China, underscore global engagement in DL-ABSA research. Major themes such as syntax and structure analysis, neural networks for sequence modeling, and specific aspects and modalities in sentiment analysis emerge from the analysis, guiding future research endeavors. The study identifies prospective avenues for practitioners, emphasizing the strategic importance of syntax analysis, neural network methodologies, and domain-specific applications. Overall, this study contributes to the understanding of DL-ABSA research dynamics, providing a roadmap for practitioners and researchers to navigate the evolving landscape and drive innovations in DL-ABSA methodologies and applications.

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Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment Analysis

Cited by 6 publications

References 0 publications

Quantifying polarization in online political discourse

Quantifying polarization in online political discourse

Design of Emotion Analysis Model IABC-Deep Learning-based for Vocal Performance

Cognitive-Inspired Deep Learning Models for Aspect-Based Sentiment Analysis: A Retrospective Overview and Bibliometric Analysis

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