Topic labeled text classification

Lecture Notes in Computer Science, 2009

In this paper we introduce and evaluate a technique for applying latent Dirichlet allocation to supervised semantic categorization of documents. In our setup, for every category an own collection of topics is assigned, and for a labeled training document only topics from its category are sampled. Thus, compared to the classical LDA that processes the entire corpus in one, we essentially build separate LDA models for each category with the category-specific topics, and then these topic collections are put together to form a unified LDA model. For an unseen document the inferred topic distribution gives an estimation how much the document fits into the category. We use this method for Web document classification. Our key results are 46% decrease in 1-AUC value in classification accuracy over tf.idf with SVM and 43% over the plain LDA baseline with SVM. Using a careful vocabulary selection method and a heuristic which handles the effect that similar topics may arise in distinct categories the improvement is 83% over tf.idf with SVM and 82% over LDA with SVM in 1-AUC. ⋆ Supported by the EU FP7 project LiWA -Living Web Archives and by grants OTKA NK 72845, ASTOR NKFP 2/004/05

Physical Review X, 2015

Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent searching, statistical characterization, and meaningful classification. Latent Dirichlet allocation (LDA) is the state of the art in topic modeling. Here, we perform a systematic theoretical and numerical analysis that demonstrates that current optimization techniques for LDA often yield results that are not accurate in inferring the most suitable model parameters. Adapting approaches from community detection in networks, we propose a new algorithm that displays high reproducibility and high accuracy and also has high computational efficiency. We apply it to a large set of documents in the English Wikipedia and reveal its hierarchical structure.

PeerJ Computer Science, 2021

In supervised machine learning, specifically in classification tasks, selecting and analyzing the feature vector to achieve better results is one of the most important tasks. Traditional methods such as comparing the features’ cosine similarity and exploring the datasets manually to check which feature vector is suitable is relatively time consuming. Many classification tasks failed to achieve better classification results because of poor feature vector selection and sparseness of data. In this paper, we proposed a novel framework, topic2features (T2F), to deal with short and sparse data using the topic distributions of hidden topics gathered from dataset and converting into feature vectors to build supervised classifier. For this we leveraged the unsupervised topic modelling LDA (latent dirichlet allocation) approach to retrieve the topic distributions employed in supervised learning algorithms. We made use of labelled data and topic distributions of hidden topics that were generat...

Indonesian Journal of Electrical Engineering and Computer Science

This work aims at discovering topics in a text corpus and classifying the most relevant terms for each of the discovered topics. The process was performed in four steps: first, document extraction and data processing; second, labeling and training of the data; third, labeling of the unseen data; and fourth, evaluation of the model performance. For processing, a total of 10,322 "curriculum" documents related to data science were collected from the web during 2018-2022. The latent dirichlet allocation (LDA) model was used for the analysis and structure of the subjects. After processing, 12 themes were generated, which allowed ranking the most relevant terms to identify the skills of each of the candidates. This work concludes that candidates interested in data science must have skills in the following topics: first, they must be technical, they must have mastery of structured query language, mastery of programming languages such as R, Python, java, and data management, among...

Text classification has become a critical step in big data analytics. For supervised machine learning approaches to text classification, availability of sufficient training data with classification labels attached to individual text units is essential to the performance. Since labeled data are usually scarce, however, it is always desirable to devise a semi-supervised method where unlabeled data are used in addition to labeled ones. A solution is to apply a latent factor model to generate clustered text features and use them for text classification. The main thrust of the current research is to extend Latent Dirichlet Allocation (LDA) for this purpose by considering word weights in sampling and maintaining balances of topic distributions. A series of experiments were conducted to evaluate the proposed method for classification tasks. The result shows that the topic distributions generated by the balance weighted topic modeling method add some discriminative power to feature generations for classification.

International Journal of Electrical and Computer Engineering (IJECE), 2018

Latent Dirichlet Allocation (LDA) is a probability model for grouping hidden topics in documents by the number of predefined topics. If conducted incorrectly, determining the amount of K topics will result in limited word correlation with topics. Too large or too small number of K topics causes inaccuracies in grouping topics in the formation of training models. This study aims to determine the optimal number of corpus topics in the LDA method using the maximum likelihood and Minimum Description Length (MDL) approach. The experimental process uses Indonesian news articles with the number of documents at 25, 50, 90, and 600; in each document, the numbers of words are 3898, 7760, 13005, and 4365. The results show that the maximum likelihood and MDL approach result in the same number of optimal topics. The optimal number of topics is influenced by alpha and beta parameters. In addition, the number of documents does not affect the computation times but the number of words does. Computational times for each of those datasets are 2.9721, 6.49637, 13.2967, and 3.7152 seconds. The optimisation model has resulted in many LDA topics as a classification model. This experiment shows that the highest average accuracy is 61% with alpha 0.1 and beta 0.001.

Lecture Notes in Computer Science, 2014

We introduce a novel text representation method to be applied on corpora containing short / medium length textual documents. The method applies Latent Dirichlet Allocation (LDA) on a corpus to infer its major topics, which will be used for document representation. The representation that we propose has multiple levels (granularities) by using different numbers of topics. We postulate that interpreting data in a more general space, with fewer dimensions, can improve the representation quality. Experimental results support the informative power of our multi-level representation vectors. We show that choosing the correct granularity of representation is an important aspect of text classification. We propose a multi-level representation, at different topical granularities, rather than choosing one level. The documents are represented by topical relevancy weights, in a low-dimensional vector representation. Finally, the proposed representation is applied to a text classification task using several well-known classification algorithms. We show that it leads to very good classification performance. Another advantage is that, with a small compromise on accuracy, our low-dimensional representation can be fed into many supervised or unsupervised machine learning algorithms that empirically cannot be applied on the conventional high-dimensional text representation methods.

Lecture Notes in Computer Science, 2013

In this article, we present a novel document annotation method that can be applied on corpora containing short documents such as social media texts. The method applies Latent Dirichlet Allocation (LDA) on a corpus to initially infer some topical word clusters. Each document is assigned one or more topic clusters automatically. Further document annotation is done through a projection of the topics extracted and assigned by LDA into a set of generic categories. The translation from the topical clusters to the small set of generic categories is done manually. Then the categories are used to automatically annotate the general topics of the documents. It is remarkable that the number of the topical clusters that need to be manually mapped to the general topics is far smaller than the number of postings of a corpus that normally need to be annotated to build training and testing sets manually. We show that the accuracy of the annotation done through this method is about 80% which is comparable with inter-human agreement in similar tasks. Additionally, using the LDA method, the corpus entries are represented by low-dimensional vectors which lead to good classification results. The lower-dimensional representation can be fed into many machine learning algorithms that cannot be applied on the conventional high-dimensional text representation methods.

Information Processing & Management, 2018

Question answering systems assist users in satisfying their information needs more precisely by providing focused responses to their questions. Among the various systems developed for such a purpose, community-based question answering has recently received researchers' attention due to the large amount of user-generated questions and answers in social question-and-answer platforms. Reusing such data sources requires an accurate information retrieval component enhanced by a question classifier. The question classification gives the system the possibility to have information about question categories to focus on questions and answers from relevant categories to the input question. In this paper, we propose a new method based on unsupervised Latent Dirichlet Allocation for classifying questions in community-based question answering. Our method first uses unsupervised topic modeling to extract topics from a large amount of unlabeled data. The learned topics are then used in the training phase to find their association with the available category labels in the training data. The category mixture of topics is finally used to predict the label of unseen data.

Indonesian Journal of Electrical Engineering and Computer Science, 2022

Recently, a probabilistic topic modelling approach, latent dirichlet allocation (LDA), has been extensively applied in the arena of document classification. However, classical LDA is an unsupervised algorithm implemented using a fixed number of topics without prior domain knowledge and generates different outcomes with the change in the order of documents. This article presents a comprehensive framework to evade the order effect and unsupervised probabilistic nature. First, the framework creates the vocabulary specific to the category using a weight-dependent model that extracts distinctive features suitable for supervised classification. Then, it transforms a classified cluster of documents from the domain corpus to the relevant topic making it more robust to noise. The framework was tested on a comprehensive collection of benchmark news datasets that vary in sample size, class characteristics, and classification tasks. In contrast to the conventional classification methods, the proposed framework achieved 95.56% and 95.23% accuracy when applied on two datasets, indicating that the proposed algorithm has a better classification capability. Furthermore, the topics extracted from the classified clusters are highly relevant to domain categories.