Review preprocessing using data cleaning and stemming technique
Sandesh Gharatkar, Aakash Ingle, Tanmay Naik, Ashwini M. Save · 2017
The opinions and experiences of other people constitute an important source of information in our everyday life. For example, we ask our friends which dentist, restaurant, or Smartphone they would recommend to us. Nowadays, online customer reviews have become an invaluable resource to answer such questions. Besides helping consumers to make more informed purchase decisions, online reviews are also of great value to vendors, as they represent unsolicited and genuine customer feedback that is conveniently available at virtually no costs. However, for popular products there often exist several thousands of reviews so that manual analysis is not an option. The reviews need cleaning before analysis the proposed system provide the architecture that cleans the unwanted data and provides quality data for analysis. The existing systems provides basic data cleaning steps such as tokenization, stop word removal, URLs removal, special character removal and hash tag removal which all are core steps in data cleaning. The proposed system implements all core steps and perform additional steps like slang word replacement, spell checker and stemming which help to produce quality data for sentiment analysis.