Date of Award
Doctor of Philosophy (PhD)
Text classification, the task of metadata to documents, needs a person to take significant time and effort. Since online-generated contents are explosively growing, it becomes a challenge for manually annotating with large scale and unstructured data. Recently, various state-or-art text mining methods have been applied to classification process based on the keywords extraction. However, when using these keywords as features in the classification task, it is common that the number of feature dimensions is large. In addition, how to select keywords from documents as features in the classification task is a big challenge. Especially, when using traditional machine learning algorithms in big data, the computation time is very long. On the other hand, about 80% of real data is unstructured and non-labeled in the real world. The conventional supervised feature selection methods cannot be directly used in selecting entities from massive data. Usually, statistical strategies are utilized to extract features from unlabeled data for classification tasks according to their importance scores. We propose a novel method to extract key features effectively before feeding them into the classification assignment. Another challenge in the text classification is the multi-label problem, the assignment of multiple non-exclusive labels to documents. This problem makes text classification more complicated compared with a single label classification. For the above issues, we develop a framework for extracting data and reducing data dimension to solve the multi-label problem on labeled and unlabeled datasets. In order to reduce data dimension, we develop a hybrid feature selection method that extracts meaningful features according to the importance of each feature. The Word2Vec is applied to represent each document by a feature vector for the document categorization for the big dataset. The unsupervised approach is used to extract features from real online-generated data for text classification. Our unsupervised feature selection method is applied to extract depression symptoms from social media such as Twitter. In the future, these depression symptoms will be used for depression self-screening and diagnosis.
Ma, Long, "A Multi-label Text Classification Framework: Using Supervised and Unsupervised Feature Selection Strategy." Dissertation, Georgia State University, 2017.