Abstract
Various aspects or characteristic features of an entity come into interplay to create an underlying fabric upon which sentiments blossom. In multi aspect Sentiment Analysis (SA), potentially related aspects of an entity under review are discussed in a single piece of text such as an online review. In this work, we use domain ontologies for enabling multi-aspect Sentiment Analysis. Since, domain ontologies contain the entire domain knowledge, they assist in enhanced aspect identification and detection of the latent or hidden aspects in a review document. We illustrate our approach by developing a system named Ontology driven Multi Aspect Sentiment Analysis (OMASA) system. We provide hotel reviews as input to this system and identify the panorama of explicitly expressed and latent aspects in a review using hotel domain ontology. After detecting the aspects, we link them with the corresponding opinions to gauge the sentiment pertaining to the aspects extracted. OMASA first computes sentiment scores for every aspect of the hotel. It then evaluates the overall sentiment score. On comparing with the baseline, the experimental results of OMASA show a marked improvement in the aspect level evaluation metrics \(\Delta_{aspect}^2\) and \(\rho_{aspect}\) after detecting the hidden aspects. This shows that OMASA has the potential to identify the latent aspects in text thereby improving the quality of SA.
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Sharma, S., Saraswat, M., Dubey, A.K. (2022). Multi-aspect Sentiment Analysis Using Domain Ontologies. In: Villazón-Terrazas, B., Ortiz-Rodriguez, F., Tiwari, S., Sicilia, MA., Martín-Moncunill, D. (eds) Knowledge Graphs and Semantic Web . KGSWC 2022. Communications in Computer and Information Science, vol 1686. Springer, Cham. https://doi.org/10.1007/978-3-031-21422-6_19
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