Micro-blog Post Topic Drift Detection Based on LDA Model

Liu, Quanchao; Huang, Heyan; Feng, Chong

doi:10.1007/978-3-319-04048-6_10

Cited by 7 publications

(2 citation statements)

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“…From literature, it is evident that the topic drift can be examined by modeling time with word co-occurrence patterns (Wang & McCallum 2006), identifying topic boundaries (Liu et al 2013), detecting the sub topics (Fei et al 2015), quantifying the impact of the topic on a location (Bernabe-Moreno et al 2015) and representing the context as a cluster of hashtags (Alam et al 2017). Social media text reflects a cultural change in the social environment that leads to topic drift where location plays a major role.…”

Section: Introductionmentioning

confidence: 99%

Joint Topical Word Embedding for Detecting Drift in Social Media Text

Vijayarani

T.V.

2020

Preprint

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Social media texts like tweets and blogs are collaboratively created by human interaction. Fast change in trends leads to topic drift in the social media text. This drift is usually associated with words and hashtags. However, geotags play an important part in determining topic distribution with location context. Rate of change in the distribution of words, hashtags and geotags cannot be considered as uniform and must be handled accordingly. This paper builds a topic model that associates topic with a mixture of distributions of words, hashtags and geotags. Stochastic gradient Langevin dynamic model with varying mini-batch sizes is used to capture the changes due to the asynchronous distribution of words and tags. Topical word embedding with co-occurrence and location contexts are specified as hashtag context vector and geotag context vector respectively. These two vectors are jointly learned to yield topical word embedding vectors related to tags context. Topical word embeddings over time conditioned on hashtags and geotags predict, location-based topical variations effectively. When evaluated with Chennai and UK geolocated Twitter data, the proposed joint topical word embedding model enhanced by the social tags context, outperforms other methods.

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Section: Introductionmentioning

confidence: 99%

Joint Topical Word Embedding for Detecting Drift in Social Media Text

Vijayarani

T.V.

2020

Preprint

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“…On the other hand, although the topic detection in text mining [8] and video processing [9] is a hot research area, the research issue, that of discovering hidden topics in graphstructured data, has not been well solved. For example, in the text mining area, if topics are from scientific papers about a research area, the topics mean the different research directions of this research area, i.e., cloud computing and machine learning; in the image mining area, if topics are about the images in a scene, the topics mean the different background semantics (e.g.…”

Section: Introductionmentioning

confidence: 99%

Topic Model for Graph Mining

Xuan

Zhang

et al. 2015

IEEE Trans. Cybern.

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Graph mining has been a popular research area because of its numerous application scenarios. Many unstructured and structured data can be represented as graphs, such as, documents, chemical molecular structures, and images. However, an issue in relation to current research on graphs is that they cannot adequately discover the topics hidden in graph-structured data which can be beneficial for both the unsupervised learning and supervised learning of the graphs. Although topic models have proved to be very successful in discovering latent topics, the standard topic models cannot be directly applied to graph-structured data due to the "bag-of-word" assumption. In this paper, an innovative graph topic model (GTM) is proposed to address this issue, which uses Bernoulli distributions to model the edges between nodes in a graph. It can, therefore, make the edges in a graph contribute to latent topic discovery and further improve the accuracy of the supervised and unsupervised learning of graphs. The experimental results on two different types of graph datasets show that the proposed GTM outperforms the latent Dirichlet allocation on classification by using the unveiled topics of these two models to represent graphs.

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