Text classification method based on self-training and LDA topic models

Pavlinek, Miha; Podgorelec, Vili

doi:10.1016/j.eswa.2017.03.020

Cited by 184 publications

(68 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Based on the study carried out for this article, it has been found that most widely used text classification techniques follow semi-supervised learning approach (Deshmukh & Tripathy, 2017;Pavlinek & Podgorelec, 2017;W. Zhang, Tang, & Yoshida, 2015).…”

Section: Resultsmentioning

confidence: 99%

Text Classification Techniques: A Literature Review

Thangaraj

Sivakami

2018

IJIKM

View full text Add to dashboard Cite

Aim/Purpose: The aim of this paper is to analyze various text classification techniques employed in practice, their strengths and weaknesses, to provide an improved awareness regarding various knowledge extraction possibilities in the field of data mining. Background: Artificial Intelligence is reshaping text classification techniques to better acquire knowledge. However, in spite of the growth and spread of AI in all fields of research, its role with respect to text mining is not well understood yet. Methodology: For this study, various articles written between 2010 and 2017 on “text classification techniques in AI”, selected from leading journals of computer science, were analyzed. Each article was completely read. The research problems related to text classification techniques in the field of AI were identified and techniques were grouped according to the algorithms involved. These algorithms were divided based on the learning procedure used. Finally, the findings were plotted as a tree structure for visualizing the relationship between learning procedures and algorithms. Contribution: This paper identifies the strengths, limitations, and current research trends in text classification in an advanced field like AI. This knowledge is crucial for data scientists. They could utilize the findings of this study to devise customized data models. It also helps the industry to understand the operational efficiency of text mining techniques. It further contributes to reducing the cost of the projects and supports effective decision making. Findings: It has been found more important to study and understand the nature of data before proceeding into mining. The automation of text classification process is required, with the increasing amount of data and need for accuracy. Another interesting research opportunity lies in building intricate text data models with deep learning systems. It has the ability to execute complex Natural Language Processing (NLP) tasks with semantic requirements. Recommendations for Practitioners: Frame analysis, deception detection, narrative science where data expresses a story, healthcare applications to diagnose illnesses and conversation analysis are some of the recommendations suggested for practitioners. Recommendation for Researchers: Developing simpler algorithms in terms of coding and implementation, better approaches for knowledge distillation, multilingual text refining, domain knowledge integration, subjectivity detection, and contrastive viewpoint summarization are some of the areas that could be explored by researchers. Impact on Society: Text classification forms the base of data analytics and acts as the engine behind knowledge discovery. It supports state-of-the-art decision making, for example, predicting an event before it actually occurs, classifying a transaction as ‘Fraudulent’ etc. The results of this study could be used for developing applications dedicated to assisting decision making processes. These informed decisions will help to optimize resources and maximize benefits to the mankind. Future Research: In the future, better methods for parameter optimization will be identified by selecting better parameters that reflects effective knowledge discovery. The role of streaming data processing is still rarely explored when it comes to text classification.

show abstract

Section: Resultsmentioning

confidence: 99%

Text Classification Techniques: A Literature Review

Thangaraj

Sivakami

2018

IJIKM

View full text Add to dashboard Cite

show abstract

“…ABOUT HERE According to Guerreiro et al (2016), the ideal number of clusters/topics is attained when the variability explained does not change significantly by adding more clusters. A small number of topics produce topics that are too general, while a big number of topics may reduce the interpretability of the results (Pavlinek & Podgorelec, 2017). The number of optimal topics (K) was selected when there was a more negative likelihood after the model first stabilized its variability and just before the log-likelihood started to increase again.…”

Section: Insert Figure 4 About Herementioning

confidence: 99%

Understanding the use of Virtual Reality in Marketing: A text mining-based review

Loureiro

Guerreiro

Eloy

et al. 2019

Journal of Business Research

238

137

View full text Add to dashboard Cite

The current study intends to highlight the most relevant studies in simulated realities with special attention to VR and marketing, showing how studies have evolved over time and discussing the findings. A text-mining approach using a Bayesian statistical topic model called latent Dirichlet allocation is employed to conduct a comprehensive analysis of 150 articles from 115 journals, all indexed in Web of Science. The findings reveal seven relevant topics, as well as the number of articles published over time, the authors most cited in VR papers and the leading journals in each topic. The article also provides theoretical and practical implications and suggestions for further research.

show abstract

“…The topic modelling technique known as LDA is used in [9,7] and considers two main concepts: 1) a single document can have several latent topics and 2) each topic can be drawn as a probability distribution of words (documents are represented as vectors of topics instead of bags of words). A supervised variant of LDA (sLDA) is proposed in [10], which incorporates a response variable (or class) when calculating the model of topics.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, topic modelling has arisen as a promising alternative to the existing methods, by achieving good results in documents classification using probability distributions similarity, dimensional reduction and incorporating topics semantic [7,8,9]. Latent Dirichlet Allocation (LDA) is one of the most popular techniques in this area, using both unsupervised and supervised learning [10].…”

Section: Introductionmentioning

confidence: 99%

Evaluación de distintas técnicas de representación de texto y medidas de distancia de texto usando KNN para clasificación de documentos

Calvo-Valverde¹,

Mena-Arias²

2020

View full text Add to dashboard Cite

Actualmente, los datos textuales constituyen una parte fundamental de las bases de datos de todo el mundo y uno de los mayores desafíos ha sido la extracción de información útil a partir de conjuntos grandes de documentos de texto. La literatura existente sobre métodos para resolver este problema es muy extensa, sin embargo, los métodos estadísticos (que utilizan métricas de similitud sobre vectores de palabras) han mostrado resultados muy favorables en el campo de la minería de texto durante los últimos 25 años. Adicionalmente, otros modelos han surgido como una prometedora alternativa para lograr reducción dimensional e incorporación de la semántica en la clasificación de documentos, tal como el modelado de temas. Este proyecto se enfoca en la evaluación de técnicas de representación y medidas de similitud de texto (Coseno, Jaccard y Kullback-Leibler) usando el algoritmo de Vecinos más Cercanos (KNN por sus siglas en inglés), con el fin de medir la efectividad del modelado de temas para reducción dimensional al clasificar texto. Los resultados muestran que la versión más tradicional del vector de palabras y la similitud Jaccard superaron al resto de las combinaciones en la mayoría de los casos de uso. Sin embargo, el análisis estadístico mostró que no hubo una diferencia significativa entre la exactitud obtenida al usar representaciones generadas por la Asignación de Dirichlet Latente (técnica de modelado de temas más conocida como LDA por sus siglas en inglés), y la obtenida usando técnicas tradicionales de clasificación de texto. LDA logró abstraer miles de palabras en menos de 60 temas para el primer conjunto de pruebas. Experimentos adicionales sugieren que el modelado de temas puede llegar a lograr un mejor rendimiento al ser usado para clasificar textos cortos y al incrementar el número de temas permitidos al momento de generar el modelo.

show abstract

Text classification method based on self-training and LDA topic models

Cited by 184 publications

References 26 publications

Text Classification Techniques: A Literature Review

Text Classification Techniques: A Literature Review

Understanding the use of Virtual Reality in Marketing: A text mining-based review

Evaluación de distintas técnicas de representación de texto y medidas de distancia de texto usando KNN para clasificación de documentos

Contact Info

Product

Resources

About