Shashank, JosephMugauri, CalvinSumathy., S2023-06-192023-06-192017-01-01: Shashank Joseph et al 2017. IOP Conf. Series: Materials Science and Engineering 263 (2017) 042011 doi:10.1088/1757-899X/263/4/042011http://localhost:8080/xmlui/handle/123456789/778Sentiment analysis of feature ranking methods for classification accuracyText pre-processing and feature selection are important and critical steps in text mining. Text pre-processing of large volumes of datasets is a difficult task as unstructured raw data is converted into structured format. Traditional methods of processing and weighing took much time and were less accurate. To overcome this challenge, feature ranking techniques have been devised. A feature set from text preprocessing is fed as input for feature selection. Feature selection helps improve text classification accuracy. Of the three feature selection categories available, the filter category will be the focus. Five feature ranking methods namely: document frequency, standard deviation information gain, CHISQUARE, and weighted-log likelihood –ratio is analyzed.endatasets, text pre-processing, feature selection, classification accuracy, feature rankingOther