CLUDS: COMBINING LABELED AND UNLABELED DATA WITH LOGISTIC REGRESSION FOR SOCIAL MEDIA ANALYSIS

Abstract

Automatic text classification and sentiment polarity detection are two important research problems of social media analysis. The meanings of the words are so important that they need to be captured by a document classification algorithm to reach an accurate classification performance. Another important issue with the text classification is the scarcity of labeled data. In this study, Combining Labeled and Unlabeled Data with Semantic Values of Terms (CLUDS) is presented. CLUDS has the following steps: preprocessing, instance labeling, combining labeled and unlabeled data, and prediction. In preprocessing step Latent Dirichlet Allocation (LDA) algorithm is used. In instance labeling step Logistic Regression is applied. In CLUDS, relevance values computation has been applied as a supervised term weighting methodology in the text classification field. Still, according to the literature, CLUDS is the first attempt that uses both relevance and weighting calculation in a semi-supervised semantic kernel for Support Vector Machines (SVM). In this study, Sprinkled-CLUDS and Adaptive-Sprinkled-CLUDS have also been implemented. Evaluated experimental results show that CLUDS, Sprinkled-CLUDS and Adaptive-Sprinkled-CLUDS generate a valuable performance gain over the baseline algorithms on test sets.