An Extensive Study On Automated Aspect And Aspect Category Summarization Technique To Influence On Sentimental Analysis Of Co-Occurrence Data

Abstract

The classification of the aspect and its category from the product and service reviews has become a primary concern for the consumer in decision making. Nowadays reviews becoming more valuable in making wise decisions. Many advanced approaches based on supervised method and unsupervised method models has helped to provide this objective in terms of summarization. The key challenges were propagating on sentence summarization and orientation. Due to unsatisfactory results, there exists an exploration for unsupervised learning model through utilization of the sentiment analysis for developing and idea in an effective way. In this paper, the detailed analysis is carried out on existing literature to identify aspects and aspect categories using unsupervised model. The aspect categories play a major role in providing useful information about the particular assumption of a certain idea. It essentially attains a useful representation of the reviews automatically and it identify the typical sentiment assigning of sentences. The aspect category is determined on basis of context co-occurrence frequency. In addition, lexical representation is carried out for each category. For analysis of each technique, the breakdowns and obtained performance are included.