Affiliations: [a]
Department of Computer Science and Engineering, A.V.C. College of Engineering, Mannampandal, Mayiladuthurai, TamilNadu, India
| [b]
Department of Computer Applications, A.V.C. College of Engineering, Mannampandal, Mayiladuthurai, TamilNadu, India
Correspondence:
[*]
Corresponding author. M. Devi Sri Nandhini, Assistant Professor, Department of Computer Science and Engineering, A.V.C. College of Engineering, Mannampandal, Mayiladuthurai, Tamil Nadu, India. E-mail: nandhini.avcce@gmail.com.
Abstract: Sentiment analysis is the contextual analysis of words to retrieve the social opinion of a brand which aids the business firms/institutions to know the impact of their products/services. It is habitual that users may express different opinions regarding various aspects of the same entity. Therefore, there is a strong demand to extract all the opinion targets may those be explicitly mentioned aspects or implicit aspects which are not directly specified in the reviews. In this context, comparatively less amount of work has been carried out concerning implicit aspect detection. The proposed work has been dedicated solely to extracting the implicit aspects using a dynamic approach based on the type of sentence containing the clues for implicit aspect. A novel aspect pointer compendium (APC) has been developed that catalyzes the task of finding implicit aspects to the maximum extent possible. The APC incorporates the usage of different types of clues such as synonym clues, context clues, phrase clues, and partially implicit aspects that aid in the detection of hidden aspects. Based on this idea, the proposed work classifies the implicit aspect sentences into six types and proceeds with the task in an efficient manner. To strengthen the task of implicit aspect detection, the proposed work utilizes a hybrid technique encompassing APC, domain-specific adjective-noun collocation list (DSANCL), and the explicit aspect-opinion word pairs extracted from the reviews. The experimentation and results reveal that the proposed hybrid approach shows a good improvement in terms of the efficacy of extracting the implicit aspects as compared to the existing baseline models.
