Lexicon-based sentiment analysis in texts using Formal Concept Analysis
Abstract
In this paper, we present a novel approach for sentiment analysis that uses Formal Concept Analysis (FCA) to create dictionaries for classification. Unlike other methods that rely on pre-defined lexicons, our approach allows for the creation of customised dictionaries that are tailored to the specific data and tasks. By using a dataset of tweets categorised into positive and negative polarity, we show that our approach achieves a better performance than other standard dictionaries.
Citation
Please, cite this work as:
[OLM23] M. Ojeda-Hernández, D. López-Rodríguez, and Á. Mora. “Lexicon-based sentiment analysis in texts using Formal Concept Analysis”. In: International Journal of Approximate Reasoning 155 (2023), pp. 104-112. ISSN: 0888-613X. DOI: https://doi.org/10.1016/j.ijar.2023.02.001. URL: https://www.sciencedirect.com/science/article/pii/S0888613X23000130.
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[1] O. N. Akande, M. O. Lawrence, and P. Ogedebe. “Application of bidirectional LSTM deep learning technique for sentiment analysis of COVID-19 tweets: post-COVID vaccination era”. In: Journal of Electrical Systems and Information Technology 10.1 (Nov. 2023). ISSN: 2314-7172. DOI: 10.1186/s43067-023-00118-w. URL: http://dx.doi.org/10.1186/s43067-023-00118-w.
[2] K. Barik and S. Misra. “Analysis of customer reviews with an improved VADER lexicon classifier”. In: Journal of Big Data 11.1 (Jan. 2024). ISSN: 2196-1115. DOI: 10.1186/s40537-023-00861-x. URL: http://dx.doi.org/10.1186/s40537-023-00861-x.
[3] S. S. Berutu, H. Budiati, J. Jatmika, et al. “Data preprocessing approach for machine learning-based sentiment classification”. In: JURNAL INFOTEL 15.4 (Nov. 2023), p. 317–325. ISSN: 2085-3688. DOI: 10.20895/infotel.v15i4.1030. URL: http://dx.doi.org/10.20895/infotel.v15i4.1030.
[4] S. Boffa and P. Murinová. “Aristotle’s square for mining fuzzy concepts”. In: Fuzzy Sets and Systems 508 (May. 2025), p. 109323. ISSN: 0165-0114. DOI: 10.1016/j.fss.2025.109323. URL: http://dx.doi.org/10.1016/j.fss.2025.109323.
[5] Y. Chen, C. Hao, A. Zheng, et al. “Emotional Orientation in Peer Assessment: Impact on College Student Performance”. In: The Asia-Pacific Education Researcher 34.2 (Jul. 2024), p. 629–647. ISSN: 2243-7908. DOI: 10.1007/s40299-024-00884-9. URL: http://dx.doi.org/10.1007/s40299-024-00884-9.
[6] Y. Cheng, C. Zhang, A. K. Sangaiah, et al. “Efficient Low-Resource Medical Information Processing Based on Semantic Analysis and Granular Computing”. In: ACM Transactions on Asian and Low-Resource Language Information Processing (Oct. 2023). ISSN: 2375-4702. DOI: 10.1145/3626319. URL: http://dx.doi.org/10.1145/3626319.
[7] T. Hidayat, A. Ahmad, and H. C. Ngo. “Non-redundant implicational base of formal context with constraints using SAT”. In: PeerJ Computer Science 10 (Jan. 2024), p. e1806. ISSN: 2376-5992. DOI: 10.7717/peerj-cs.1806. URL: http://dx.doi.org/10.7717/peerj-cs.1806.
[8] C. F. Ho, K. L. Chean, and T. M. Lim. “Leveraging Machine Translation to Enhance Sentiment Analysis on Multilingual Text”. In: Proceedings of the 2024 13th International Conference on Software and Computer Applications. ICSCA 2024. ACM, Feb. 2024, p. 242–248. DOI: 10.1145/3651781.3651819. URL: http://dx.doi.org/10.1145/3651781.3651819.
[9] K. Liagkouras and K. Metaxiotis. “Extracting Sentiment from Business News Announcements for More Efficient Decision Making”. In: Advances in Artificial Intelligence-Empowered Decision Support Systems. Springer Nature Switzerland, 2024, p. 263–282. ISBN: 9783031623165. DOI: 10.1007/978-3-031-62316-5_11. URL: http://dx.doi.org/10.1007/978-3-031-62316-5_11.
[10] H. Liu, X. Xin, W. Peng, et al. “Concept-driven knowledge distillation and pseudo label generation for continual named entity recognition”. In: Expert Systems with Applications 270 (Apr. 2025), p. 126546. ISSN: 0957-4174. DOI: 10.1016/j.eswa.2025.126546. URL: http://dx.doi.org/10.1016/j.eswa.2025.126546.
[11] S. Liu and Q. Liu. “A sentiment analysis model based on dynamic pre-training and stacked involutions”. In: The Journal of Supercomputing 80.11 (Apr. 2024), p. 15613–15635. ISSN: 1573-0484. DOI: 10.1007/s11227-024-06052-6. URL: http://dx.doi.org/10.1007/s11227-024-06052-6.
[12] X. Liu, M. Li, Y. Ma, et al. “Personalized tourism product design focused on tourist expectations and online reviews: An integrated MCDM method”. In: Computers & Industrial Engineering 188 (Feb. 2024), p. 109860. ISSN: 0360-8352. DOI: 10.1016/j.cie.2023.109860. URL: http://dx.doi.org/10.1016/j.cie.2023.109860.
[13] Y. Liu, T. You, J. Zou, et al. “Modelling customer requirement for mobile games based on online reviews using BW-CNN and S-Kano models”. In: Expert Systems with Applications 258 (Dec. 2024), p. 125142. ISSN: 0957-4174. DOI: 10.1016/j.eswa.2024.125142. URL: http://dx.doi.org/10.1016/j.eswa.2024.125142.
[14] D. López-Rodríguez, M. Ojeda-Hernández, and T. Pattison. “Systems of implications obtained using the Carve decomposition of a formal context”. In: Knowledge-Based Systems (Apr. 2025), p. 113475. ISSN: 0950-7051. DOI: 10.1016/j.knosys.2025.113475. URL: http://dx.doi.org/10.1016/j.knosys.2025.113475.
[15] H. Montesinos-Yufa and E. Musgrove. “A Sentiment Analysis of News Articles Published Before and During the COVID-19 Pandemic”. In: International Journal on Data Science and Technology 10.2 (Aug. 2024), p. 38–44. ISSN: 2472-2200. DOI: 10.11648/j.ijdst.20241002.13. URL: http://dx.doi.org/10.11648/j.ijdst.20241002.13.
[16] N. Smatov, R. Kalashnikov, and A. Kartbayev. “Development of Context-Based Sentiment Classification for Intelligent Stock Market Prediction”. In: Big Data and Cognitive Computing 8.6 (May. 2024), p. 51. ISSN: 2504-2289. DOI: 10.3390/bdcc8060051. URL: http://dx.doi.org/10.3390/bdcc8060051.
[17] Y. Wang and K. Wang. “Will Public Health Emergencies Affect Compensatory Consumption Behavior? Evidence from Emotional Eating Perspective”. In: Foods 13.22 (Nov. 2024), p. 3571. ISSN: 2304-8158. DOI: 10.3390/foods13223571. URL: http://dx.doi.org/10.3390/foods13223571.
[18] G. Yu, Y. Yong, C. Jiang, et al. “Formal concept analysis assisted large-scale global optimization and its application to cloud task scheduling”. In: Complex & Intelligent Systems 11.6 (Apr. 2025). ISSN: 2198-6053. DOI: 10.1007/s40747-025-01878-w. URL: http://dx.doi.org/10.1007/s40747-025-01878-w.
[19] C. Zhang, Q. Wen, D. Li, et al. “Intelligent evaluation system for new energy vehicles based on sentiment analysis: An MG-PL-3WD method”. In: Engineering Applications of Artificial Intelligence 133 (Jul. 2024), p. 108485. ISSN: 0952-1976. DOI: 10.1016/j.engappai.2024.108485. URL: http://dx.doi.org/10.1016/j.engappai.2024.108485.
[20] L. Zhang and Y. Jiang. “Fusing semantic aspects for formal concept analysis using knowledge graphs”. In: Multimedia Tools and Applications 83.6 (Jul. 2023), p. 16763–16797. ISSN: 1573-7721. DOI: 10.1007/s11042-023-16271-3. URL: http://dx.doi.org/10.1007/s11042-023-16271-3.