A Comparison of Topic Modelling Approaches for Urdu Text

Munir, Siraj; Wasi, Shaukat; Jami, Syed Imran

doi:10.17485/ijst/2019/v12i45/145722

Cited by 5 publications

(2 citation statements)

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“…The coherence score and topic quality were used as evaluation metrics, and this ascertained LDA produced the top-quality terms which are interpretable with competitive coherence score. This outcome is consistent with that of Ray et al (2019), Munir et al (2019), Albalawi et al (2020), M'sik & Casablanca (2020 and Kherwa & Bansal (2020) that ascertained LDA as the leading statistical topic modelling technique in their comparative studies.…”

Section: Evaluation Of the Topic Modelssupporting

confidence: 91%

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

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Banking is an essential component of our day-to-day activity, and the sector contributes to the development of every Nation's economy. To ensure bank stability, the banks have gone through several deregulation and reformation processes. Unfortunately, these processes and technological advancement have led to increased competitiveness, saturated market, and low profitability. Thus, the need for banks to build customer centric service to gain profitability and stability is vital. This opens the need to investigate customers attitude towards banking.With the growing usage of social network sites (SNS), the user generated content (UGC) has opened opportunities for banks to mine customers opinion . This can help the banks to generate insight into their product and services, create marketing strategies and manage their reputation. To the customers, this serves as source to information that can help in supporting decision making on product or service purchase. In this study, sentiment analysis (SA) techniques were employed to investigate customers attitude towards banking us ing Twitter data. However, the unstructured nature of the data and word ambiguity made sentiment analysis complicated. In the context of this study, SA is more difficult because it involves the natural language processing of Pidgin English and English words in the bank domain.Unfortunately, there are limited or no resources for this purpose.

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Section: Evaluation Of the Topic Modelssupporting

confidence: 91%

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

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“…-e contributions of the presented work are the use of cosine similarity with n-gram-based LDA topics to measure similarity in vectors of text documents. Achievement of overall 84.52% accuracy on PAN12 datasets and 93.17% accuracy on Urdu news articles without using any labels for authorship identification task is done (Munir et al, 2019). experimented topic modeling approaches for Urdu poetry text to show that these approaches perform equally well in any genre of text.…”

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confidence: 99%

Urdu News Clustering Using K-Mean Algorithm On The Basis Of Jaccard Coefficient And Dice Coefficient Similarity

Rahman

Hussain

Shah

et al. 2022

ADCAIJ

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Clustering is the unsupervised machine learning process that group data objects into clusters such that objects within the same cluster are highly similar to one another. Every day the quantity of Urdu text is increasing at a high speed on the internet. Grouping Urdu news manually is almost impossible, and there is an utmost need to device a mechanism which cluster Urdu news documents based on their similarity. Clustering Urdu news documents with accuracy is a research issue and it can be solved by using similarity techniques i.e., Jaccard and Dice coefficient, and clustering k-mean algorithm. In this research, the Jaccard and Dice coefficient has been used to find the similarity score of Urdu News documents in python programming language. For the purpose of clustering, the similarity results have been loaded to Waikato Environment for Knowledge Analysis (WEKA), by using k-mean algorithm the Urdu news documents have been clustered into five clusters. The obtained cluster’s results were evaluated in terms of Accuracy and Mean Square Error (MSE). The Accuracy and MSE of Jaccard was 85% and 44.4%, while the Accuracy and MSE of Dice coefficient was 87% and 35.76%. The experimental result shows that Dice coefficient is better as compared to Jaccard similarity on the basis of Accuracy and MSE.

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Analyzing LDA and NMF Topic Models for Urdu Tweets via Automatic Labeling

Zoya

Latif

2021

IEEE Access

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Various topic modeling methods provide a means of understanding and analyzing content available on social media platforms like Twitter and Facebook in an unsupervised manner. However, despite several existing conventional techniques, they have had limited success when applied directly for filtering and quick comprehension of short-text contents due to text sparseness and noise. Thus, it always has been a challenging problem to discover reliable latent topics from online discussion texts that prevailed with low words co-occurrence and availability of large size social media benchmark datasets even for resource-rich languages. The existing literature lacks such work for Urdu text to unveil niche topics even with conventional topic models mainly due to lack of benchmark datasets, limited availability of pre-processing tools/ algorithms, and time and compute limitations on large size datasets. This work presents experiments with multiple approaches of topic modeling like Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA), and Non-negative Matrix Factorization (NMF) on 0.8 million Urdu tweets. These tweets are collected through Twitter API by giving various hashtags as a query to avoid dominance of single topic in the dataset. In addition, we have pre-processed the text of the tweets, prepared the three variants of the collected dataset, and extracted multiple features to represent documents on different n-grams. Furthermore, all these techniques are compared and evaluated on the dataset variants using both qualitative and quantitative measures. We have also demonstrated the results of these approaches through visualization methods, graphs depicting tweets size per topic, word clouds, and hashtags analysis giving insights about algorithms performances on finalized topics. Observed results reveal that NMF outperformed aa the techniques with TF-IDF feature vectors on Urdu tweets text while LDA performed best with merging shorttext strategy into long pseudo documents.

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A Comparison of Topic Modelling Approaches for Urdu Text

Cited by 5 publications

References 0 publications

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Urdu News Clustering Using K-Mean Algorithm On The Basis Of Jaccard Coefficient And Dice Coefficient Similarity

Analyzing LDA and NMF Topic Models for Urdu Tweets via Automatic Labeling

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