Affiliations: [a]
Department of Computer Science and Engineering, A.V.C. College of Engineering, Mannampandal, Mayiladuthurai, Tamil Nadu, India
| [b]
Department of Computer Applications, A.V.C. College of Engineering, Mannampandal, Mayiladuthurai, Tamil Nadu, India
Correspondence:
[*]
Corresponding author. Devi Sri Nandhini M, Research Scholar, Anna University, Chennai, Tamil Nadu, India. E-mail: nandhini.avcce@gmail.com.
Abstract: Since people express their opinions and feelings more openly than ever before, sentiment analysis proves to be a promising research area that effectively analyses the opinion expressed over the entities. In this context, Sentiment analysis is utilized to gather valuable insights from users’ opinions. These insights would benefit a lot for the business concerns and institutions to improve their respective products/services. Aspect-based sentiment analysis (ABSA) is the most robust technique that offers a more fine-grained analysis. The objective of this paper is to improve the efficacy of ABSA by framing a robust and enhanced set of rules. Several experiments were carried out to detect explicit and implicit aspects. The hybrid approach comprising of enhanced rule-based approach (ERBA) and domain-specific lexicon (DSL) is used to improve the solution of the aspect-based sentiment analysis problem. The proposed approach employs a domain-specific adjective-noun collocation list(DSANCL) tailored to the domain for fine-tuning the process of implicit aspect detection(IAD). The proposed model frames a new nine-point scale for measuring the sentiment strength by introducing a ternary classification of intensifiers based on their degree of intensification. The performance of the proposed model is evaluated using the university reviews dataset.
