A Robust Pre-processing model for Textual Data Analysis using PCL-COVID-19 Datasets

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Mrs. U. Hemamalini, Dr. S. Perumal


Global access to internet and flexibility enrolled with it attracted many peoples utilizing the benefit of its usage in many ways. Peoples started utilizing the services of internet with flexible and user friendly options. Peoples have their own freedom on conveying their opinion on products that they were experienced in the form of reviews. These online reviews act as one of the game changing factor for many products that would reach the customers or creates a great impact on particular incident, speech towards the society etc. Analysing those textual data in a specific approach is mandatory nowadays to improve the online image of the organization. The commonly used tweets, reviews impact the decision making angle of the users widely.  The user feedbacks written by the consumers normally looks as a unstructured text data, hence to extract the real impacted emotion of the given comments,  these reviews are required to be cleaned up. Pre-processing of original unstructured data is act as an important step of Data Mining. The main objective of this research paper to examine several pre-processing and feature selection techniques along with feature representation methods. The paper is focused on discussing various opinion mining levels available and the learning techniques adopted for sentiment analysis (SA). The raw data from the twitter is cleaned in many ways before handling the data for classification.  The current paper formulates the Sample data from PANCEALAB website on COVID-19 discussions.

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