A Detailed Survey on Topic Modeling for Document and Short Text Data

Likhitha, S; Junior, B. S.; H., M.,

doi:10.5120/ijca2019919265

Cited by 33 publications

(29 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They structured the social network analysis methods into two types, namely, structural analysis methods (which study the structure of the social network like friendships), and added-content methods (which study the content added by users). Likhitha et al ( 2019 ) presented a detailed survey covering the various TM techniques in social media text and summarized many applications, quantitative evaluations of various methods, and many datasets that are used with various challenges in short content and documents. Table 1 presents several related works that reviewed the TM methods in long/short textual social media data.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis

Albalawi

Yeap

Benyoucef

2020

Front. Artif. Intell.

226

130

View full text Add to dashboard Cite

With the growth of online social network platforms and applications, large amounts of textual user-generated content are created daily in the form of comments, reviews, and short-text messages. As a result, users often find it challenging to discover useful information or more on the topic being discussed from such content. Machine learning and natural language processing algorithms are used to analyze the massive amount of textual social media data available online, including topic modeling techniques that have gained popularity in recent years. This paper investigates the topic modeling subject and its common application areas, methods, and tools. Also, we examine and compare five frequently used topic modeling methods, as applied to short textual social data, to show their benefits practically in detecting important topics. These methods are latent semantic analysis, latent Dirichlet allocation, non-negative matrix factorization, random projection, and principal component analysis. Two textual datasets were selected to evaluate the performance of included topic modeling methods based on the topic quality and some standard statistical evaluation metrics, like recall, precision, F -score, and topic coherence. As a result, latent Dirichlet allocation and non-negative matrix factorization methods delivered more meaningful extracted topics and obtained good results. The paper sheds light on some common topic modeling methods in a short-text context and provides direction for researchers who seek to apply these methods.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis

Albalawi

Yeap

Benyoucef

2020

Front. Artif. Intell.

226

130

View full text Add to dashboard Cite

show abstract

“…To summarize a subtitle file, the authors extracted the informative sentences from the original document using a topic modelling technique. Topic modelling is an unsupervised learning approach that aims to organize and classify textual data by discovering words' patterns [25]. Additionally, identifying keywords, i.e., topics, help in determining valuable sentences.…”

Section: Lda-based Subtitles Summarization Modelmentioning

confidence: 99%

Educational Videos Subtitles’ Summarization Using Latent Dirichlet Allocation and Length Enhancement

Alrumiah¹,

Al-Shargabi²

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Nowadays, people use online resources such as educational videos and courses. However, such videos and courses are mostly long and thus, summarizing them will be valuable. The video contents (visual, audio, and subtitles) could be analyzed to generate textual summaries, i.e., notes. Videos' subtitles contain significant information. Therefore, summarizing subtitles is effective to concentrate on the necessary details. Most of the existing studies used Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA) models to create lectures' summaries. This study takes another approach and applies Latent Dirichlet Allocation (LDA), which proved its effectiveness in document summarization. Specifically, the proposed LDA summarization model follows three phases. The first phase aims to prepare the subtitle file for modelling by performing some preprocessing steps, such as removing stop words. In the second phase, the LDA model is trained on subtitles to generate the keywords list used to extract important sentences. Whereas in the third phase, a summary is generated based on the keywords list. The generated summaries by LDA were lengthy; thus, a length enhancement method has been proposed. For the evaluation, the authors developed manual summaries of the existing "EDUVSUM" educational videos dataset. The authors compared the generated summaries with the manual-generated outlines using two methods, (i) Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and (ii) human evaluation. The performance of LDA-based generated summaries outperforms the summaries generated by TF-IDF and LSA. Besides reducing the summaries' length, the proposed length enhancement method did improve the summaries' precision rates. Other domains, such as news videos, can apply the proposed method for video summarization.

show abstract

“…For a document d , collections D and for each word w W in that document, the probability of that word w belonging to each topic z i Z is computed [43]. Mathematically,…”

Section: Evaluate (Testing)mentioning

confidence: 99%

Topic Modeling for Amharic User Generated Texts

2021

View full text Add to dashboard Cite

Topic Modeling is a statistical process, which derives the latent themes from extensive collections of text. Three approaches to topic modeling exist, namely, unsupervised, semi-supervised and supervised. In this work, we develop a supervised topic model for an Amharic corpus. We also investigate the effect of stemming on topic detection on Term Frequency Inverse Document Frequency (TF-IDF) features, Latent Dirichlet Allocation (LDA) features and a combination of these two feature sets using four supervised machine learning tools, that is, Support Vector Machine (SVM), Naive Bayesian (NB), Logistic Regression (LR), and Neural Nets (NN). We evaluate our approach using an Amharic corpus of 14,751 documents of ten topic categories. Both qualitative and quantitative analysis of results show that our proposed supervised topic detection outperforms with an accuracy of 88% by SVM using state-of-the-art-approach TF-IDF word features with the application of the Synthetic Minority Over-sampling Technique (SMOTE) and with no stemming operation. The results show that text features with stemming slightly improve the performance of the topic classifier over features with no stemming.

show abstract

A Detailed Survey on Topic Modeling for Document and Short Text Data

Abstract: Text mining is one of the most significant field in the digital era due to the rapid growth of textual information. Topic models are gaining popularity in the last few years.

Cited by 33 publications

References 70 publications

Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis

Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis

Educational Videos Subtitles’ Summarization Using Latent Dirichlet Allocation and Length Enhancement

Topic Modeling for Amharic User Generated Texts

Contact Info

Product

Resources

About