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CEA-TM: A Customer Experience Analysis Framework Based on Contextual-Aware Topic Modeling Approach

Abstract : Text mining comprises different techniques capable to perform text analysis, information retrieval and extraction, categorization and visualization, is experiencing an increase of interest. Among these techniques, topic modeling algorithms, capable of discovering topics from large documents corpora, has many applications. In particular, considering customer experience analysis, having access to topic coherent set of opinions expressed in terms of text reviews, has an important role in both customers side and business providers. Traditional topic modeling algorithms are probabilistic models words co-occurrences oriented which can mislead topics discovery in case of short-text and context-base reviews. In this paper, we propose a customer experience analysis framework which enrich a state-of-art topic modeling algorithm (LDA) with a semantic-base topic-tuning approach.
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https://hal.inria.fr/hal-03287672
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Submitted on : Thursday, July 15, 2021 - 6:10:23 PM
Last modification on : Friday, August 13, 2021 - 4:29:53 PM
Long-term archiving on: : Saturday, October 16, 2021 - 7:04:51 PM

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Ariona Shashaj, Davide Stirparo, Mohammad Kazemi. CEA-TM: A Customer Experience Analysis Framework Based on Contextual-Aware Topic Modeling Approach. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.659-672, ⟨10.1007/978-3-030-79150-6_52⟩. ⟨hal-03287672⟩

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