Data mining is a procedure of extracting the requisite
information from unprocessed records by using certain
methodologies and techniques. Data having sentiments of customers is of utmost
importance for managers and decision-makers who intend to monitor the progress,
to maintain the quality of their products or services and to observe the latest market
trends for business support. Billions of customers are using micro-blogging
websites and social media for sharing their opinions about different topics on daily
basis. Therefore, it has become a source of acquiring information but to identify a
particular feature of a product is still an issue as the information retrieves from
varied sources. We proposed a framework for data acquisition, preprocessing,
feature extraction and used three supervised
machine-learning algorithms for classification of
customers’ sentiments. The proposed framework
also tested to evaluate the system’s performance.
Our proposed methodology will be helpful for
researchers, service providers, and decisionmakers.
1-Dost Muhammad Khan Assistant Professor,Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Punjab, Pakistan. 2-Tariq Aziz Rao Visiting Lecturer,Department of Computer Science,Virtual University of Pakistan, Lahore, Punjab, Pakistan. 3-Faisal Shahzad Lecturer, Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Punjab, Pakistan.