Abstract
In this paper, we investigate the issue of modeling corporate entities’ online reputation. We introduce a bayesian latent probabilistic model approach for e-Reputation analysis based on Dimensions (Reputational Concepts) Categorization and Opinion Mining from textual content. Dimensions to analyze e-Reputation are set up by analyst as latent variables. Machine Learning (ML) Natural Language Processing (NLP) approaches are used to label large sets of text passages. For each Dimension, several estimations of the relationship with each text passage are computed as well as Opinion and Priority. The proposed automatic path modeling algorithm explains Opinion or Priority scores based on selected Dimensions. Model Robustness’ is evaluated over RepLab dataset.
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This work is funded by the project ImagiWeb ANR-2012-CORD-002-01.
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Cossu, JV., SanJuan, E., Torres-Moreno, JM., El-Bèze, M. (2015). Multi-dimensional Reputation Modeling Using Micro-blog Contents. In: Esposito, F., Pivert, O., Hacid, MS., Rás, Z., Ferilli, S. (eds) Foundations of Intelligent Systems. ISMIS 2015. Lecture Notes in Computer Science(), vol 9384. Springer, Cham. https://doi.org/10.1007/978-3-319-25252-0_48
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