Trending topic discovery of Twitter Tweets using clustering and topic modeling algorithms

Sapul, Ma. Shiela C.; Aung, Than Htike; Jiamthapthaksin, Rachsuda

doi:10.1109/jcsse.2017.8025911

Cited by 12 publications

(3 citation statements)

References 2 publications

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“…This approach is basically very useful for search engines, to automate the customer service and other areas where knowing the topics from texts is crucial. There are several algorithms available that be trained for topic modelling such as LDA, LSA, NMF and Clustering [34][35][36][37][38][39][40][41][42][43][44]. These algorithms are unsupervised methods, which means, the relationship among document is not revealed prior to the model being executed.…”

Section: Topic Modellingmentioning

confidence: 99%

Exploring the Non-Medical impacts of Covid-19 using Natural Language Processing

Agade¹,

Balpande²

2020

Preprint

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Ongoing COVID-19 Pandemic has resulted into massive damage to various platforms of global economy which has caused disruption to human livelihood. Natural Language Processing has been extensively used in different organizations to categorize sentiments, perform recommendation, summarizing information and topic modelling. This research aims to understand the non-medical impact of COVID-19 on global economy by leveraging the natural language processing methodology. This methodology comprises of text classification which includes topic modelling on unstructured COVID-19 media articles dataset provided by Anacode. Like other Natural Language Processing algorithms, Latent Dirichlet allocation (LDA) and Non-negative matrix factorization (NMF) has been proposed to classify the media articles dataset in order to analyze COVID-19 pandemic impacts in the different sectors of global economy. Model Accuracy was examined based on the coherence and perplexity score which came out to be 0.51 and -10.90 using LDA algorithm. Both the LDA and NMF algorithm identified similar prevalent topics that was impacted by COVID-19 pandemic in multiple sectors of economy. Through intertopic distance map visualization produced by LDA algorithm, it can be reciprocated that general industries which includes children schooling, parental care, and family gatherings had the major impact followed by business sector and the financial industry.

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Section: Topic Modellingmentioning

confidence: 99%

Exploring the Non-Medical impacts of Covid-19 using Natural Language Processing

Agade¹,

Balpande²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…HGTM uses tags relational knowledge to analyze the semantic relations between users and their hashtags to develop more reliable topics even if words are unavailable in the specific tweets. Instead of using only hashtags, Sapul et al, [73] introduced a CLOPE algorithm, a logic of using hashtags with each keyword in a text which extracts more accurate feature and results with more relevant topics based on the available feature words. Qiu et al, [67] proposed a Dynamic Social Network Topic Model (DSM) to group cluster based on topics scattered by users' interest and also their connectivity with social networks.…”

Section: Topic Modeling On Short Textsmentioning

confidence: 99%

“…[15], [19], [22], [23], [27] , [32], [35], [44][45][46], [51], [60][66], [67], [72], [73], [75], [80], [82], [83] Open Directory Project (ODP) - [37] Tweets related to Apple, Google, Microsoft, Twitter - [36], [70] Weibo Collections - [15], [35] Amazon Reviews (May 1994 -June 2014 contains product reviews) -7 [41] [45]…”

mentioning

confidence: 99%