A Survey on Journey of Topic Modeling Techniques from SVD to Deep Learning

Sharma, Deepak Kumar; Kumar, B. Shravan; Chand, Satish

doi:10.5815/ijmecs.2017.07.06

Cited by 25 publications

(4 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In stage one, we find an extended list of topics, T ext , for the whole cluster. To do this, we use the commonly-used LDA Bag-of-Words model [10], and we focus on the titles of the threads in the cluster threads because the titles provide a compact and meaningful summary of the threads. In stage two, we calculate the relevance scores of each thread with respect to each topic t ∈ T ext .…”

Section: Step 3: Investigation Of Clustersmentioning

confidence: 99%

TenFor: A Tensor-Based Tool to Extract Interesting Events from Security Forums

Islam¹,

Rokon²,

Papalexakis³

et al. 2020

Preprint

View full text Add to dashboard Cite

How can we get a security forum to "tell" us its activities and events of interest? We take a unique angle: we want to identify these activities without any a priori knowledge, which is a key difference compared to most of the previous problem formulations. Despite some recent efforts, mining security forums to extract useful information has received relatively little attention, while most of them are usually searching for specific information. We propose TenFor, an unsupervised tensor-based approach, to systematically identify important events in a three-dimensional space: (a) user, (b) thread, and (c) time. Our method consists of three high-level steps: (a) a tensor-based clustering across the three dimensions, (b) an extensive cluster profiling that uses both content and behavioral features, and (c) a deeper investigation, where we identify key users and threads within the events of interest. In addition, we implement our approach as a powerful and easy-to-use platform for practitioners. In our evaluation, we find that 83% of our clusters capture meaningful events and we find more meaningful clusters compared to previous approaches. Our approach and our platform constitute an important step towards detecting activities of interest from a forum in an unsupervised learning fashion in practice.

show abstract

Section: Step 3: Investigation Of Clustersmentioning

confidence: 99%

TenFor: A Tensor-Based Tool to Extract Interesting Events from Security Forums

Islam¹,

Rokon²,

Papalexakis³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Since its invention a couple of decades ago, topic modeling-a technique based on co-occurrence data-has routinely been applied to reveal thematic patterns "hidden" in diverse and ever-growing collections of texts [1,2]. Besides text documents, topic models were built to classify image [3], video [4], audio [5], and time-series [6] data, and have become an indispensable tool of business intelligence [7,8].…”

Section: Introduction and Literature Survey 1backgroundmentioning

confidence: 99%

A Text Segmentation Approach for Automated Annotation of Online Customer Reviews, Based on Topic Modeling

2022

View full text Add to dashboard Cite

Online customer review classification and analysis have been recognized as an important problem in many domains, such as business intelligence, marketing, and e-governance. To solve this problem, a variety of machine learning methods was developed in the past decade. Existing methods, however, either rely on human labeling or have high computing cost, or both. This makes them a poor fit to deal with dynamic and ever-growing collections of short but semantically noisy texts of customer reviews. In the present study, the problem of multi-topic online review clustering is addressed by generating high quality bronze-standard labeled sets for training efficient classifier models. A novel unsupervised algorithm is developed to break reviews into sequential semantically homogeneous segments. Segment data is then used to fine-tune a Latent Dirichlet Allocation (LDA) model obtained for the reviews, and to classify them along categories detected through topic modeling. After testing the segmentation algorithm on a benchmark text collection, it was successfully applied in a case study of tourism review classification. In all experiments conducted, the proposed approach produced results similar to or better than baseline methods. The paper critically discusses the main findings and paves ways for future work.

show abstract

“…In contrary manual tagging, is very extensive and needs expertise in the documents of subject-matter proficient, whereas the algorithmic based analysis is an automatic process [5,6,7] known as topic modeling. The detailed understanding of the topic modeling is available in some past surveys that include [8,9,10,11,12]. In survey paper [8,11], presents a classification of directed probabilistic topic models and explains a broader view on graphical models.…”

Section: Introductionmentioning

confidence: 99%

“…In [9] and [10] a preliminary discussion about topic modeling is presented. In paper [12] discusses the classification of probabilistic topic modeling algorithms and the journey of topic modeling techniques from its initial model till the advance models using deep learning.…”

Section: Introductionmentioning

confidence: 99%

A Trend Analysis of Machine Learning Research with Topic Models and Mann-Kendall Test

Sharma¹,

Kumar²,

Chand³

2019

IJISA

Self Cite

View full text Add to dashboard Cite

This paper aims to systematically examine the literature of machine learning for the period of 1968~2017 to identify and analyze the research trends. A list of journals from well-established publishers ScienceDirect, Springer, JMLR, IEEE (approximately 23,365 journal articles) related to machine learning is used to prepare a content collection. To the best of our information, it is the first effort to comprehend the trend analysis in machine learning research with topic models: Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and LDA with Coherent Model (LDA_CM). The LDA_CM topic model gives the highest topic coherence amongst all topic models under consideration. This study provides a scientific ground that helps to overcome the subjectivity of collective opinion. The Mann-Kendall test is used to understand the trend of the topics. Our findings provide indicative of paradigmatic shifts in research methodology of significant patterns of topical prominence and the evolving research areas. It is used to highlight the evolution regarding the previous and recent trends in research topics in the area of machine learning. Understanding such an intellectual structure and future trends will assist the researchers to adopt the divergent developments of this research in one place. This paper analyzes the overall trends of the machine learning research since 1968, based on the latent topics identified in the period of 2007~2017 that may be helpful to the researchers exploring the recommended areas and publish their research articles.

show abstract

A Survey on Journey of Topic Modeling Techniques from SVD to Deep Learning

Cited by 25 publications

References 49 publications

TenFor: A Tensor-Based Tool to Extract Interesting Events from Security Forums

TenFor: A Tensor-Based Tool to Extract Interesting Events from Security Forums

A Text Segmentation Approach for Automated Annotation of Online Customer Reviews, Based on Topic Modeling

A Trend Analysis of Machine Learning Research with Topic Models and Mann-Kendall Test

Contact Info

Product

Resources

About