An Algorithmic Approach based on Principal Component Analysis for Aspect-based Opinion Summarization

Summarization helps in reducing the text in the shortest way possible such that significant properties in the text remain preserved and important information can be gained from the text. A novel approach is developed and summaries are generated using extractive based technique using Principal Component Analysis. The advantages of the proposed method lie in greater computational efficiency, data understanding, robustness and handling sparse data. This paper discusses the aspect based opinion summarization problem by proposing a novel unsupervised method by combining theories of rational awareness with sentence dependency trees to identify aspects. The results are carried out on dissimilar datasets consisting of numerous opinions and comparison with the previous based approaches demonstrates the success of the work. The results on Opinosis dataset are reported by measuring using ROUGE tool. The three random individuals are contacted for reference summaries which are compared with the system generated gold summaries for conducting subjective evaluation.