SentiQ: A Probabilistic Logic Approach to Enhance Sentiment Analysis Tool Quality - Université Paris Cité Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

SentiQ: A Probabilistic Logic Approach to Enhance Sentiment Analysis Tool Quality

Wissam Mammar Kouadri
  • Fonction : Auteur
  • PersonId : 1106342
Salima Benbernou
  • Fonction : Auteur
  • PersonId : 1077018
Mourad Ouziri
  • Fonction : Auteur
Themis Palpanas
Iheb Benamor
  • Fonction : Auteur
  • PersonId : 1106343

Résumé

The opinion expressed in various Web sites and social-media is an essential contributor to the decision making process of several organizations. Existing sentiment analysis tools aim to extract the polarity (i.e., positive, negative, neutral) from these opinionated contents. Despite the advance of the research in the field, sentiment analysis tools give inconsistent polarities, which is harmful to business decisions. In this paper, we propose SentiQ, an unsupervised Markov logic Network-based approach that injects the semantic dimension in the tools through rules. It allows to detect and solve inconsistencies and then improves the overall accuracy of the tools. Preliminary experimental results demonstrate the usefulness of SentiQ.
Fichier principal
Vignette du fichier
wisdom2020kouadri(17) (1).pdf (566.98 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03299105 , version 1 (26-07-2021)

Identifiants

  • HAL Id : hal-03299105 , version 1

Citer

Wissam Mammar Kouadri, Salima Benbernou, Mourad Ouziri, Themis Palpanas, Iheb Benamor. SentiQ: A Probabilistic Logic Approach to Enhance Sentiment Analysis Tool Quality. WISDOM 2020 : The 9th KDD Workshop on Issues of Sentiment Discovery and Opinion Mining, Aug 2020, San Diego, United States. ⟨hal-03299105⟩
29 Consultations
30 Téléchargements

Partager

Gmail Facebook X LinkedIn More