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.

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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.

BibTeX

@article{ijar2023,
    title = {Lexicon-based sentiment analysis in texts using Formal Concept Analysis},
    journal = {International Journal of Approximate Reasoning},
    volume = {155},
    pages = {104-112},
    year = {2023},
    issn = {0888-613X},
    doi = {https://doi.org/10.1016/j.ijar.2023.02.001},
    url = {https://www.sciencedirect.com/science/article/pii/S0888613X23000130},
    author = {Manuel Ojeda-Hernández and Domingo López-Rodríguez and Ángel Mora},
    keywords = {Formal Concept Analysis, Sentiment analysis, Polarity analysis, Text mining, Lexicon}
}