Idea mining is a new and interesting field in the areas of information retrieval research. The thoughts of people are helpful to improve strategic decision making. This paper demonstrates the efficient computational methods of idea characterization based concept by extracting the interesting hidden data from unstructured texts which come in many forms and sizes. It may be stored in patents, publications, reports, documents, Internet etc. We briefly discussed a number of successful text mining tools and text classification to extract the idea with a combination of idea mining measures.

The number of users of an on-line shopping websites is continuously increasing. Such website often provides facility for the users to give comments and ratings to the products being sold on the websites. This information can be useful as the recommendation for other users in making their purchase decision. This paper investigates the problem of predicting rating based on users' comments. A classifier based on information retrieval model is proposed for the prediction. In addition, the effect of integrating sentiment analysis for the rating prediction is also investigated. Based on the results, an improvement in prediction performance can be expected with sentiment analysis where an increase of 54% is achieved.

Abstracts of research papers are meant to provide a brief condensed overview of respective research topics. This includes a glimpse of the new idea that the paper proposes. The aim of the research presented here is to investigate the feasibility of the effect of text position in the idea identification. The abstracts are structured in the form of introduction, body, and conclusion. It is hypothesized that research ideas tend to be phrased in conclusion section of paper abstracts. 25 abstracts of the scientific papers were used to automatically identify the position of ideas within abstract sections. The results support the notion that the conclusion of the abstracts significantly represents the ideas.

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