For Searching and managing online growth of information is becoming a difficult task. The major challenge is to improve users search experience. The current technique that is involved in Content description and query processing in Information Retrieval (IR) are based on keywords. I am therefore trying to improve the quality of search results. In this paper I am trying to optimize the search engines results. Mostly used search engines are Google, Yahoo and Bing. Thus the query q is provided as an input to search engine followed by retrieving relevant ddocuments/links to user. Depending upon the user behavior the documents are retrieved to user. For this we will firstly create a login section where user will provide interests, hobbies and designation in it, to make searching more useful.

Text based Semantic information predictions using user behavior

In recent years, the volume of data submissions (E-Commerce data) in online on products, service, and organizations is increasing exponentially. This online data is abundantly unstructured; extracting knowledge from that huge volume of data is a non-trivial task. In recent years, extracting product names become a very popular approach and also one of the important methods in sentiment analysis. This product name extraction is very useful in E-commerce, because it helps in identifying people interest on products, generation of reviews' metadata and identification of product attributes, etc. The existing approaches in product name extraction are capable of extracting single word product names. However, the product names can be a sequence of words, which is also called multi-word product names that cannot be obtained automatically by the existing methods. In this paper, a combined approach of semi-supervised machine learning and improved C-value approach is proposed to discover the multi-word product names, ranking those product names and identifying a dominant product in review documents.

An empirical semi-supervised machine learning approach on extracting and ranking document level multi-word product names using improved C-value approach

R. Sivashankari

Papers

An empirical semi-supervised machine learning approach on extracting and ranking document level multi-word product names using improved C-value approach