Targeted aspects oriented topic modeling for short texts

He, Jin; Li, Lei; Wang, Yan; Wu, Xindong

doi:10.1007/s10489-020-01672-w

Cited by 11 publications

(10 citation statements)

References 33 publications

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“…GPU-DMM uses the same model assumptions of DMM (Yin and Wang, 2014). Similarly, the Targeted Aspects Oriented Topic Modeling (TATM) is based on the enhanced DMM process (He et al, 2020). They both assume that each document only has one topic.…”

Section: Related Workmentioning

confidence: 99%

SenU-PTM: a novel phrase-based topic model for short-text topic discovery by exploiting word embeddings

Zhang

2021

DTA

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PurposeTopic model has been widely applied to discover important information from a vast amount of unstructured data. Traditional long-text topic models such as Latent Dirichlet Allocation may suffer from the sparsity problem when dealing with short texts, which mostly come from the Web. These models also exist the readability problem when displaying the discovered topics. The purpose of this paper is to propose a novel model called the Sense Unit based Phrase Topic Model (SenU-PTM) for both the sparsity and readability problems.Design/methodology/approachSenU-PTM is a novel phrase-based short-text topic model under a two-phase framework. The first phase introduces a phrase-generation algorithm by exploiting word embeddings, which aims to generate phrases with the original corpus. The second phase introduces a new concept of sense unit, which consists of a set of semantically similar tokens for modeling topics with token vectors generated in the first phase. Finally, SenU-PTM infers topics based on the above two phases.FindingsExperimental results on two real-world and publicly available datasets show the effectiveness of SenU-PTM from the perspectives of topical quality and document characterization. It reveals that modeling topics on sense units can solve the sparsity of short texts and improve the readability of topics at the same time.Originality/valueThe originality of SenU-PTM lies in the new procedure of modeling topics on the proposed sense units with word embeddings for short-text topic discovery.

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Section: Related Workmentioning

confidence: 99%

SenU-PTM: a novel phrase-based topic model for short-text topic discovery by exploiting word embeddings

Zhang

2021

DTA

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“…Document is represented as a mixture of topics where a topic is defined by a probability distribution over the set of terms. In our case, the corpus is composed of a set of Facebook posts as short texts He et al (2020) so that each post represents a text document and the LDA-based topic modelling is applied on them in order to discover the main discussed topics during COVID-19 disease.…”

Section: Covid-19 Related Social Data Analysis Research Workmentioning

confidence: 99%

Multilingual topic modelling for tracking COVID-19 trends based on Facebook data analysis

Amara

Aouicha

2020

Preprint

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Social data has shown important role in tracking, monitoring and risk management of disasters. Indeed, several works focused on the benets of social data analysis to the healthcare practices and curing. Similarly, these data are exploited now for tracking the COVID-19 pandemic but the majority of works exploited twitter as source. In this paper, we choose to exploit Facebook, rarely used, for tracking the evolution of COVID-19 related trends. In fact, a multilingual dataset covering 7 languages (English (EN), Arabic (AR), Spanish (ES), Italian (IT), German (DE), French (FR) and Japanese (JP)) is extracted from Facebook public posts. The proposal is an analytics process including a data gathering step, pre-processing, LDA-based topic modelling and presentation module using graph structure. Data analysing covers the duration spanned from January 1st, 2020 to May 15, 2020 divided on three periods in cumulative way: rst period January-February, second period March-April and the last one to 15 May. The results showed that the extracted topics correspond to the chronological development of what has been circulated around the pandemic and the measures that have been taken in the various languages under discussion.

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“…Fortunately, many novel techniques can be employed to quantify qualitative data [ 7 ]. Recently, it becomes a hot spot in converting the qualitative analysis of politics, disasters, and emergencies into quantitative information [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Forecasting oil consumption with attention-based IndRNN optimized by adaptive differential evolution

Wang

et al. 2022

Appl Intell

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Accurate prediction of oil consumption plays a dominant role in oil supply chain management. However, because of the effects of the coronavirus disease 2019 (COVID-19) pandemic, oil consumption has exhibited an uncertain and volatile trend, which leads to a huge challenge to accurate predictions. The rapid development of the Internet provides countless online information (e.g., online news) that can benefit predict oil consumption. This study adopts a novel news-based oil consumption prediction methodology–convolutional neural network (CNN) to fetch online news information automatically, thereby illustrating the contribution of text features for oil consumption prediction. This study also proposes a new approach called attention-based JADE-IndRNN that combines adaptive differential evolution (adaptive differential evolution with optional external archive, JADE) with an attention-based independent recurrent neural network (IndRNN) to forecast monthly oil consumption. Experimental results further indicate that the proposed news-based oil consumption prediction methodology improves on the traditional techniques without online oil news significantly, as the news might contain some explanations of the relevant confinement or reopen policies during the COVID-19 period.

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Targeted aspects oriented topic modeling for short texts

Cited by 11 publications

References 33 publications

SenU-PTM: a novel phrase-based topic model for short-text topic discovery by exploiting word embeddings

SenU-PTM: a novel phrase-based topic model for short-text topic discovery by exploiting word embeddings

Multilingual topic modelling for tracking COVID-19 trends based on Facebook data analysis

Forecasting oil consumption with attention-based IndRNN optimized by adaptive differential evolution

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