Comparison of Text Mining Tools, Techniques and Issues
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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.
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