Abstract
The exponential growth of social media users has changed the dynamics of retrieving the potential information from user-generated content and transformed the paradigm of information-retrieval mechanism with the novel developments on the concept of “web of data”. In this regard, our proposed Ontology-Based Sentiment Analysis provides two novel approaches: First, the emotion extraction on tweets related to COVID-19 is carried out by a well-formed taxonomy that comprises possible emotional concepts with fine-grained properties and polarized values. Second, the potential entities present in the tweet can be analyzed for semantic associativity. The extraction of emotions can be performed in two cases: (i) words directly associated with the emotional concepts present in the taxonomy and (ii) words indirectly present in the emotional concepts. Though the latter case is very challenging in processing the tweets to find the hidden patterns and extract the meaningful facts associated with it, our proposed work is able to extract and detect almost 81% of true positives and considerably able to detect the false negatives. Finally, the proposed approach's superior performance is witnessed from its comparison with other peer-level approaches.
Highlights
Emotion Analysis helps us to understand and to retrieve the potential emotional information from any user-generated content
Our proposed work is able to extract and identify almost 81% of true positives (TP) but it has encountered some serious challenges in detecting the false negatives (FN)
The proposed Emotion Ontology model has been robust and efficient in extracting the full range of human emotions pertaining to COVID-19-related concepts
Summary
Emotion Analysis helps us to understand and to retrieve the potential emotional information from any user-generated content. The ontology-based model helps to extract the emotions from the tweets based on the factors, such as concepts, the relationship between the concepts, characteristics of individual concepts, and external source document support for disambiguation. In this connection, Semantic Web technologies have been used to construct the ontology for extracting the emotions from the text documents and allow for sharing and reusing of the potential data for various applications. The rest of the paper is organized as follows: Section Related Works summarizes the existing works based on semantic similarity measures, ontological features, emotion analysis, and sentiment analysis of social media content. Section Conclusion delineates the polarity calculation and determines the performance of emotion ontology for the fine-grained measures
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
