Topic Chains for Understanding a News Corpus

Kim, Dongwoo; Oh, Alice

doi:10.1007/978-3-642-19437-5_13

Cited by 41 publications

(31 citation statements)

References 14 publications

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“…The topic probability distribution associated with each document is considered place semantics for the corresponding venue. For LDA, we set the hyper-parameters α and β to 0.1 and 0.01 respectively, which are commonly used [4], and the number of topics to 50, which is also common in this type of work.…”

Section: Discovering Place Semantics 31 Topic Discoverymentioning

confidence: 99%

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Topic-based place semantics discovered from microblogging text messages

Kim

Ihm

Myaeng

2014

Proceedings of the 23rd International Conference on World Wide Web

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Location-based social network services (LBSNS) such as Foursquare are getting the highlight with the extensive spread of GPS-enabled mobile devices, and a large body of research has been conducted to devise methods for understanding and clustering places. However, in previous studies, the predefined set of semantic categories of places play a critical role in both discovery and evaluation of the results, despite its limited ability to represent the dynamics of the places. We explore beyond the predefined semantic categories of the places and discover topicbased place semantics through the use of Latent Dirichlet Allocation, by extracting topics from the text which people post on site. We also show the proposed method allows for understanding the temporal dynamics of the place semantics. The finding of this study is intended for, but not limited to, context aware services and place recommendation systems.

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Section: Discovering Place Semantics 31 Topic Discoverymentioning

confidence: 99%

“…Since our place semantics are expressed in terms of probability distributions over topics, similarity between two venues can be measured by Jensen-Shannon (JS) divergence [4], most appropriate for computing similarity based on topic distributions. Fig.…”

Section: Venue Similaritymentioning

confidence: 99%

Topic-based place semantics discovered from microblogging text messages

Kim

Ihm

Myaeng

2014

Proceedings of the 23rd International Conference on World Wide Web

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“…Event detection consists of detecting temporal bursts of highly correlated documents [18,36,37], while event extraction aims at obtaining useful knowledge regarding these events [21,29]. Event evolution [34,38] and topic evolution [16,17,33] study how these abstract concepts (events and topics) evolve over time, based on latent relations within the corpus and temporal information encoded in each document. e event prediction through text mining problem has also been addressed in the literature.…”

Section: Related Workmentioning

confidence: 99%

Analyzing evolving stories in news articles

Barranco

Boedihardjo

Hossain

2017

Int J Data Sci Anal

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ere is an overwhelming number of news articles published every day around the globe. Following the evolution of a news-story is a di cult task given that there is no such mechanism available to track back in time to study the di usion of the relevant events in digital news feeds. e techniques developed so far to extract meaningful information from a massive corpus rely on similarity search, which results in a myopic loopback to the same topic without providing the needed insights to hypothesize the origin of a story that may be completely di erent than the news today. In this paper, we present an algorithm that mines historical data to detect the origin of an event, segments the timeline into disjoint groups of coherent news articles, and outlines the most important documents in a timeline with a so probability to provide a be er understanding of the evolution of a story.alitative and quantitative approaches to evaluate our framework demonstrate that our algorithm discovers statistically signi cant and meaningful stories in reasonable time. Additionally, a relevant case study on a set of news articles demonstrates that the generated output of the algorithm holds the promise to aid prediction of future entities in a story.

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“…Other more commercial systems such as RecordedFuture [6] and Palantir [20] use massive intelligence and information gathering, with inference and analysis executed (with the help of experts) in response to specific keywords in user queries. Finally, topic chain models [12], [19], [13] track topics across time using a similarity metric based on LDA to identify the general topics and short-term issues. All the systems mentioned above, except topic chains, are query-driven.…”

Section: Introductionmentioning

confidence: 99%

Semantic knowledge inference from online news media using an LDA-NLP approach

Doumit

Minai

2011

The 2011 International Joint Conference on Neural Networks

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The amount of news delivered by the different media in the current environment can be overwhelming. Although the events being reported are factually the same, the ways with which the news is delivered vary with the media sources involved. In many cases, it is difficult to reliably uncover the latent information hidden within the news reports due to the great diversity of topics and the sheer volume of news. Analysis of the news media has always been of interest to news analysts, politicians and policy makers in order to aggregate and make sense of the information generated every day. News sources try to achieve relevance to their audiences by providing them with news that the audience wants or finds interesting, but often also have implicit motives such as shaping the perceptions of their audience. Although these agendas or target audiences are not explicitly identified, we consider ways in which this information can be inferred by applying the tools of natural language processing and semantic analysis to the news streams from these sources.

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Topic Chains for Understanding a News Corpus

Cited by 41 publications

References 14 publications

Topic-based place semantics discovered from microblogging text messages

Topic-based place semantics discovered from microblogging text messages

Analyzing evolving stories in news articles

Semantic knowledge inference from online news media using an LDA-NLP approach

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