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This paper presents a novel approach for explainability in financial analysis by deriving financially-explainable statistical relationships through aspect-based sentiment analysis, Pearson correlation, Granger causality & uncertainty coefficient.
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R. Satapathy, I. Chaturvedi, E. Cambria, S. S. Ho, and J. C. Na, “Subjectivity detection in nuclear energy tweets,” Computación y Sistemas , vol. 21, no. 4, pp. 657–664, 2017
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Y. Chen, R. M. Rabbani, A. Gupta, and M. J. Zaki, “Comparative text analytics via topic modeling in banking,” in 2017 IEEE Symposium Series on Computational Intelligence (SSCI) . IEEE, 2017, pp. 1–8
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E. Loginova, W. K. Tsang, G. van Heijningen, L.-P. Kerkhove, and D. F. Benoit, “Forecasting directional bitcoin price returns using aspect-based sentiment analysis on online text data,” Machine Learning , pp. 1–24, 2021
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K. Kim, D. Ryu, and H. Yang, “Information uncertainty, investor sentiment, and analyst reports,” International Review of Financial Analysis , vol. 77, p. 101835, 2021
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2021
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A. Valdivia, V. Luzón, E. Cambria, and F. Herrera, “Consensus vote models for detecting and filtering neutrality in sentiment analysis,” Information Fusion , vol. 44, pp. 126–135, 2018
2018
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2018
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L. Luo, X. Ao, F. Pan, J. Wang, T. Zhao, N. Yu, and Q. He, “Beyond polarity: Interpretable financial sentiment analysis with hierarchical query-driven attention.” in IJCAI , 2018, pp. 4244–4250
2018
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Y. Ruan, A. Durresi, and L. Alfantoukh, “Using twitter trust network for stock market analysis,” Knowledge-Based Systems , vol. 145, pp. 207–218, 2018
2018
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J. C. Reboredo and A. Ugolini, “The impact of twitter sentiment on renewable energy stocks,” Energy economics , vol. 76, pp. 153–169, 2018
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C. Rudin, “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,” Nature machine intelligence , vol. 1, no. 5, pp. 206–215, 2019
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F. Xing, F. Pallucchini, and E. Cambria, “Cognitive-inspired domain adaptation of sentiment lexicons,” Information Processing and Management , vol. 56, no. 3, pp. 554–564, 2019
2019
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X. Man, T. Luo, and J. Lin, “Financial sentiment analysis (FSA): A survey,” in 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS) . IEEE, 2019, pp. 617–622
2019
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B. Liang, H. Su, L. Gui, E. Cambria, and R. Xu, “Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks,” Knowledge-Based Systems , vol. 235, p. 107643, 2022
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I. Hamraoui and A. Boubaker, “Impact of twitter sentiment on stock price returns,” Social Network Analysis and Mining , vol. 12, no. 1, p. 28, 2022
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J. Birru and T. Young, “Sentiment and uncertainty,” Journal of Financial Economics , vol. 146, no. 3, pp. 1148–1169, 2022
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G. Li, A. Zhang, Q. Zhang, D. Wu, and C. Zhan, “Pearson correlation coefficient-based performance enhancement of broad learning system for stock price prediction,” IEEE Transactions on Circuits and Systems II: Express Briefs , vol. 69, no. 5, pp. 2413–2417, 2022
2022
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E. Cambria, R. Mao, M. Chen, Z. Wang, and S.-B. Ho, “Seven pillars for the future of AI,” IEEE Intelligent Systems , vol. 38, no. 6, 2023
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Y. Ma, R. Mao, Q. Lin, P. Wu, and E. Cambria, “Multi-source aggregated classification for stock price movement prediction,” Information Fusion , vol. 91, pp. 515–528, 2023
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Z. Wang, Z. Hu, F. Li, S.-B. Ho, and E. Cambria, “Learning-based stock trending prediction by incorporating technical indicators and social media sentiment,” Cognitive Computation , vol. 15, no. 3, pp. 1092–1102, 2023
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H. Turbé, M. Bjelogrlic, C. Lovis, and G. Mengaldo, “Evaluation of post-hoc interpretability methods in time-series classification,” Nature Machine Intelligence , vol. 5, no. 3, pp. 250–260, 2023
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F. Xing, I. Chaturvedi, E. Cambria, A. Hussain, and B. Schuller, “Guest editorial: Neurosymbolic AI for sentiment analysis,” IEEE Transactions on Affective Computing , vol. 14, no. 4, 2023
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