Abstract
The popularity and the great success of social networks are due to their ability to offer Internet users a free space for expression where they can produce a large amount of information. Thus the new challenges of information research and data mining are to extract and analyze this mass of information which can then be used in different applications. This information is characterized mainly by incompleteness, imprecision, and heterogeneity. Indeed the task of analysis using models based on statistics and word frequencies is crucial. To solve the problem of uncertainty, the possibility theory turns out to be the most adequate. In this article, we propose a new approach to find relevant short texts such as tweets using the dual possibility and necessity. Our goal is to translate the fact that a tweet can only be relevant if there is not only a semantic relationship between the tweet and the query but also a synergy between the terms of the tweet. We have modeled the problem through a possibility network to measure the possibility of the relevance of terms in relation to a concept of a given query and a necessity network to measure the representativeness of terms in a tweet. The evaluation shows that using the theory of possibilities with a set of concepts relevant to an initial query gives the best precision rate compared to other approaches.
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Meriem, A.B., Hlaoua, L., Romdhane, L.B. (2020). Tweet Relevance Based on the Theory of Possibility. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_17
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DOI: https://doi.org/10.1007/978-3-030-63820-7_17
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