Dirichlet belief networks for topic structure learning

Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics has not yet been rigorously studied. Here we propose a new multi-layer generative process on word distributions of topics, where each layer consists of a set of topics and each topic is drawn from a mixture of the topics of the layer above. As the topics in all layers can be directly interpreted by words, the proposed model is able to discover interpretable topic hierarchies. As a self-contained module, our model can be flexibly adapted to different kinds of topic models to improve their modelling accuracy and interpretability. Extensive experiments on text corpora demonstrate the advantages of the proposed model.Comment: accepted in NIPS 201

Analyzing Main Topics Regarding The Electronic Information and Transaction Act in Instagram Using Latent Dirichlet Allocation

Indonesia is currently experiencing its fourth industrial revolution in the 21st century. With the introduction of the internet, Indonesia is expected to gain more than a hundred billion US Dollars and twenty-six million job openings by 2030. The rising usage of information technology prompts regulators to develop The Electronic Information Transaction Act to protect the populace from cybercrime. However, the law attracts numerous criticism due to its vague interpretation. This resulted in numerous arrests of innocents throughout Indonesia. Thus, the public is trying to voice their opinions on social media for the sake of preventing any more cases in the future. The usage of Latent Dirichlet Allocation could provide numerous benefits for this research. The separation between latent topics among random mixtures helps to identify the common ground and correlation between each post. These latent topics will be elaborated with a sample post to provide insights and expectations of the public towards the law

Exploratory Analysis of Highly Heterogeneous Document Collections

We present an effective multifaceted system for exploratory analysis of highly heterogeneous document collections. Our system is based on intelligently tagging individual documents in a purely automated fashion and exploiting these tags in a powerful faceted browsing framework. Tagging strategies employed include both unsupervised and supervised approaches based on machine learning and natural language processing. As one of our key tagging strategies, we introduce the KERA algorithm (Keyword Extraction for Reports and Articles). KERA extracts topic-representative terms from individual documents in a purely unsupervised fashion and is revealed to be significantly more effective than state-of-the-art methods. Finally, we evaluate our system in its ability to help users locate documents pertaining to military critical technologies buried deep in a large heterogeneous sea of information.Comment: 9 pages; KDD 2013: 19th ACM SIGKDD Conference on Knowledge Discovery and Data Minin

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.Comment: 8 pages, Advances in Social Networks Analysis and Mining (ASONAM), 2017 IEEE/ACM International Conferenc

Metric for seleting the number of topics in the LDA Model

The latest technological trends are driving a vast and growing amount of textual data. Topic modeling is a useful tool for extracting information from large corpora of text. A topic template is based on a corpus of documents, discovers the topics that permeate the corpus and assigns documents to those topics. The Latent Dirichlet Allocation (LDA) model is the main, or most popular, of the probabilistic topic models. The LDA model is conditioned by three parameters: two Dirichlet hyperparameters (α and β ) and the number of topics (K). Determining the parameter K is extremely important and not extensively explored in the literature, mainly due to the intensive computation and long processing time. Most topic modeling methods implicitly assume that the number of topics is known in advance, thus considering it demands an exogenous parameter. That is annoying, leaving the technique prone to subjectivities. The quality of insights offered by LDA is quite sensitive to the value of the parameter K, and perhaps an excess of subjectivity in its choice might influence the confidence managers put on the techniques results, thus undermining its usage by firms. This dissertation’s main objective is to develop a metric to identify the ideal value for the parameter K of the LDA model that allows an adequate representation of the corpus and within a tolerable elapsed time of the process. We apply the proposed metric alongside existing metrics to two datasets. Experiments show that the proposed method selects a number of topics similar to that of other metrics, but with better performance in terms of processing time. Although each metric has its own method for determining the number of topics, some results are similar for the same database, as evidenced in the study. Our metric is superior when considering the processing time. Experiments show this method is effective.As tendências tecnológicas mais recentes impulsionam uma vasta e crescente quantidade de dados textuais. Modelagem de tópicos é uma ferramenta útil para extrair informações relevantes de grandes corpora de texto. Um modelo de tópico é baseado em um corpus de documentos, descobre os tópicos que permeiam o corpus e atribui documentos a esses tópicos. O modelo de Alocação de Dirichlet Latente (LDA) é o principal, ou mais popular, dos modelos de tópicos probabilísticos. O modelo LDA é condicionado por três parâmetros: os hiperparâmetros de Dirichlet (α and β ) e o número de tópicos (K). A determinação do parâmetro K é extremamente importante e pouco explorada na literatura, principalmente devido à computação intensiva e ao longo tempo de processamento. A maioria dos métodos de modelagem de tópicos assume implicitamente que o número de tópicos é conhecido com antecedência, portanto, considerando que exige um parâmetro exógeno. Isso é um tanto complicado para o pesquisador pois acaba acrescentando à técnica uma subjetividade. A qualidade dos insights oferecidos pelo LDA é bastante sensível ao valor do parâmetro K, e pode-se argumentar que um excesso de subjetividade em sua escolha possa influenciar a confiança que os gerentes depositam nos resultados da técnica, prejudicando assim seu uso pelas empresas. O principal objetivo desta dissertação é desenvolver uma métrica para identificar o valor ideal para o parâmetro K do modelo LDA que permita uma representação adequada do corpus e dentro de um tempo de processamento tolerável. Embora cada métrica possua método próprio para determinação do número de tópicos, alguns resultados são semelhantes para a mesma base de dados, conforme evidenciado no estudo. Nossa métrica é superior ao considerar o tempo de processamento. Experimentos mostram que esse método é eficaz