Exploring Time-Sensitive Variational Bayesian Inference LDA for Social Media Data

Fang, Anjie; Macdonald, Craig; Ounis, Iadh; Habel, Philip; Xiao, Yang

doi:10.1007/978-3-319-56608-5_20

Cited by 11 publications

(11 citation statements)

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“…Later, Maiya et al [5] adopted the Hellinger distance metric to calculate the similarity between topics. On the other hand, the most common distance metric used in the literature is the cosine similarity [6,10,11]. Indeed, the cosine similarity has been shown to provide superior performance compared to other divergence-based metrics [7].…”

Section: Related Workmentioning

confidence: 99%

Evaluating Similarity Metrics for Latent Twitter Topics

Wang

Fang

Ounis

et al. 2019

Lecture Notes in Computer Science

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Topic modelling approaches such as LDA, when applied on a tweet corpus, can often generate a topic model containing redundant topics. To evaluate the quality of a topic model in terms of redundancy, topic similarity metrics can be applied to estimate the similarity among topics in a topic model. There are various topic similarity metrics in the literature, e.g. the Jensen Shannon (JS) divergence-based metric. In this paper, we evaluate the performances of four distance/divergence-based topic similarity metrics and examine how they align with human judgements, including a newly proposed similarity metric that is based on computing word semantic similarity using word embeddings (WE). To obtain human judgements, we conduct a user study through crowdsourcing. Among various insights, our study shows that in general the cosine similarity (CS) and WE-based metrics perform better and appear to be complementary. However, we also find that the human assessors cannot easily distinguish between the distance/divergence-based and the semantic similarity-based metrics when identifying similar latent Twitter topics.

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Section: Related Workmentioning

confidence: 99%

Evaluating Similarity Metrics for Latent Twitter Topics

Wang

Fang

Ounis

et al. 2019

Lecture Notes in Computer Science

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“…Scholars have thoroughly analysed sampling-based topic modelling approaches for various text corpora including social media; however, another LDA topic modelling implementation-Variational Bayesian (VB)-has not been well studied, despite its known efficiency and its adaptability to the volume and dynamics of social media data. Anjie explained how he examined the performance of the VB-based topic modelling approach for producing coherent topics, and then how he extended the VB approach by including time-sensitivity [7]. His new approach incorporated the time so as to increase the quality of the generated topics.…”

Section: An Initial Investigation Into Fixed and Adaptive Stopping Stmentioning

confidence: 99%

Report on the Information Retrieval Festival (IRFest2017)

et al. 2017

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The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017). OverviewOn the 7th of April, 2017, the 2nd SICSA Scottish Information Retrieval (IR) Workshop was held at the University of Glasgow. The workshop was co-organised by the University of Glasgow and the University of Strathclyde, and was funded by the Scottish Informatics and Computing Science Alliance (SISCA), Glasgow School of Computing and supported by ACM SIGIR.The aim of the workshop was to increase awareness and interaction between IR-related researchers working in, across, and with Scottish and UK universities.The workshop had a total of 75 attendees from across the Scottish universities and beyond. The scientific program included keynotes, industry talks, lightening academic talks and a poster/demo session as follows: 2 academic keynotes, 4 industrial talks and 12 invited academic talks from 7 different research institutions

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“…In this paper, we explore a more effective method for leveraging time features. The beta distribution is reported to effectively model time series data [2,5,12]. Hence, we propose to use the beta distribution to model the popularity curve of a hashtag topic over time.…”

Section: Introductionmentioning

confidence: 99%