Text Mining

Apart from data mining of string patterns from data sets, another example of research thesis

could be “TEXT MINING ON ON-LINE BOOKSHOP CUSTOMER REVIEWS”.

As the number of products being sold online increases, it becomes increasingly difficult for customers to make purchasing decisions based on only pictures and short product descriptions. On the other hand, customer reviews, particularly the text describing the features, comparisons and experiences of using a particular product provide a rich source of information to compare books and make purchasing decisions. Different customers may be interested in different types of books, and their preferences may vary accordingly. A feature-based book ranking technique that mines thousands of customer reviews can be implemented. First, book features within a product category should be identified and their frequencies and relative usage should be analyzed. For each feature, subjective and comparative sentences should be identified in reviews. Then sentiment orientations can be assigned to these sentences. Customer sentiments are analyzed based on their given comments on books using a text mining tool which should be developed. Start simple and gradually work up to develop it more, meeting the desired requirements.  This tool will have an important impact in decision making for both customers and the owner of the online bookshop. For implementing the whole work, it can be considered in three phases. These three phases are- backend design, frontend design and text mining. Here backend  can be designed with SQL Server, front end in ASP.NET, and necessary algorithms like Porter Stemming Algorithm as well as others, and simply, Microsoft Word can be used like other helping tools.