Comparison of Text Mining Tools, Techniques and Issues

Abstract

Now-a-days, online reviews in the e-commerce website are increasingly written by the consumers of the product. More than 80 percent of the data present in them is unstructured. These reviews have become an important source of information for the new customers to research about these products online. The curious customer research often leads to decision making towards purchasing the product based on online reviews. In contrast to structured data, unstructured data such as texts, speech, videos and pictures do not come with a data model that enables a computer to use them directly. Nowadays, computers can interpret the knowledge encoded in unstructured data using methods from text analytics, image recognition and speech recognition. Therefore, unstructured data are used increasingly in decision-making processes. But although decisions are commonly based on unstructured data, data quality assessment methods for unstructured data are lacking. While databases store only structured data, most of the data is unstructured like text documents, web pages, emails etc. Text mining is what is required if useful information needs to be extracted from tons of text. But where to begin, what are the popular tools, which techniques are used, what are the features. Beginning is always the toughest, so in this work tries to explore the tools available for text mining to help new researchers and practitioners in the field of text mining.