Text Classification from positive and unlabeled examples using Support Vector Machine (SVM)

Support Vector Machines (SVMs) are a powerful machine learning algorithm that can be used for text classification. Traditional SVMs require both positive and negative examples to train the model. However, in many real-world scenarios, it can be difficult or expensive to obtain negative examples. This study explores the application of SVMs in text classification when only positive and unlabeled examples are available. The results showed that the proposed approach achieved competitive performance compared to traditional supervised methods, even when trained on limited labeled examples. The utilization of SVC in the proposed approach is twofold. First, the SVC model is used to classify the unlabeled examples as positive or negative. Second, the SVC model is used to select the positive examples that are added to the training set. This iterative process of training and selecting examples helps to improve the classification accuracy of the SVM model. The proposed approach is a promising method for text classification when only positive and unlabeled examples are available. The approach is effective in achieving competitive performance compared to traditional supervised methods, even when trained on limited labeled examples. This work contributes to enhancing text classification techniques, particularly in situations with resource constraints and challenging label acquisition.