Association on Supervised Term Weighting Method for Classification on Data Twitter

Abstract

Term weighting is a preprocessing phase that has an important role in the text classification by giving the appropriate weight for each term in all documents. In previous research, many supervised term weighting methods have been introduced, but most of the supervised term weighting only considers the distribution of terms in the two classes so that it is not optimal for the multi-class classification. This paper introduces a new supervised weighting with association concept to optimize term weighting distributions in multi-class cases by considering terms that exist in each class and paying attention to the number of terms in the document belonging to the class, also considering the relationship pattern between one or more items with association concept in a dataset to measure the strength of terms in a class by using confidence values. The dataset used are the data twitter taken from the PR FM twitter account. The proposed supervised term weighting method implemented with SVM classifier can outperform unsupervised weighting schemes such as TF-IDF with the average accuracy 81.704%.

News Location Classification

“…The higher the heaviness of a term is, the higher is its significance. Izzah and Girsang (2021) utilized the weighting plan with a SVM classifier and 10 overlay cross-approval procedure to group text, in which the recurrence of a word was partitioned by archive length, and afterward the heaviness of each word in the record was determined. The weighting plan utilized in this study was changed Term Recurrence Converse Report Recurrence (TF-IDF-Assoc).…”