LPTA: A Probabilistic Model for Latent Periodic Topic Analysis

Yin, Zhijun; Cao, Liangliang; Zhai, ChengXiang; Huang, Thomas S.

doi:10.1109/icdm.2011.96

Cited by 16 publications

(7 citation statements)

References 31 publications

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“…Kawamae [21] introduced trend class to capture the trending topics and proposed Trend Analysis Model (TAM). Yin et al [22] proposed Latent Periodic Topic Analysis (LPTA) model which exploits the periodicity of terms. Yuan et al [23] proposed a unified generative model W 4 (Who, Where, When and What) that captured user, location, time, and activity jointly.…”

Section: Viralmentioning

confidence: 99%

Discovering Temporal Retweeting Patterns for Social Media Marketing Campaigns

Liu

et al. 2014

2014 IEEE International Conference on Data Mining

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Social media has become one of the most popular marketing channels for many companies, which aims at maximizing their influence by various marketing campaigns conducted from their official accounts on social networks. However, most of these marketing accounts merely focus on the contents of their tweets. Less effort has been made on understanding tweeting time, which is a major contributing factor in terms of attracting customers' attention and maximizing the influence of a social marketing campaign. To that end, in this paper, we provide a focused study of temporal retweeting patterns and their influence on social media marketing campaigns. Specifically, we investigate the users' retweeting patterns by modeling their retweeting behaviors as a generative process, which considers temporal, social, and topical factors. Moreover, we validate the predictive power of the model on the dataset collected from Sina Weibo, the most popular microblog platform in China. By discovering the temporal retweeting patterns, we analyze the temporal popular topics and recommend tweets to users in a time-aware manner. Finally, experimental results show that the proposed algorithm outperforms other baseline methods. This model is applicable for companies to conduct their marketing campaigns at the right time on social media.

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Section: Viralmentioning

confidence: 99%

Discovering Temporal Retweeting Patterns for Social Media Marketing Campaigns

Liu

et al. 2014

2014 IEEE International Conference on Data Mining

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“…This helped build topic chronology which again selects the time slice discretely. Yin et al, [22] proposed a latent periodic topic analysis, a variant of the LDA model, where their model exploits periodicity based on co-occurrence. This results in finding periodic topics.…”

Section: Related Workmentioning

confidence: 99%

An N-Gram Topic Model for Time-Stamped Documents

Jameel

Lam

2013

Lecture Notes in Computer Science

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Abstract. This paper presents a topic model that captures the temporal dynamics in the text data along with topical phrases. Previous approaches have relied upon bag-of-words assumption to model such property in a corpus. This has resulted in an inferior performance with less interpretable topics. Our topic model can not only capture changes in the way a topic structure changes over time but also maintains important contextual information in the text data. Finding topical n-grams, when possible based on context, instead of always presenting unigrams in topics does away with many ambiguities that individual words may carry. We derive a collapsed Gibbs sampler for posterior inference. Our experimental results show an improvement over the current state-of-theart topics over time model.

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“…Third, few of these existing studies takes into consideration temporal information regarding documents and their citations in a document network. Several studies have previously explored the temporal information for the topic modeling purpose (Blei & Lafferty, ; Daud, Li, Zhou, & Muhammad, ; Iwata, Yamada, Sakurai, & Ueda, ; Lin et al, ; Wang & McCallum, ; Yin, Cao, Han, Zhai, & Huang, ). These studies mainly focus on how topic‐word distributions evolve with time but do not consider how the influence of a document's topic distribution on the distribution of another document evolves with time.…”

Section: Related Workmentioning

confidence: 99%

A local context‐aware LDA model for topic modeling in a document network

Liu

2017

Asso for Info Science & Tech

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With the rapid development of the Internet and its applications, growing volumes of documents increasingly become interconnected to form large‐scale document networks. Accordingly, topic modeling in a network of documents has been attracting continuous research attention. Most of the existing network‐based topic models assume that topics in a document are influenced by its directly linked neighbouring documents in a document network and overlook the potential influence from indirectly linked ones. The existing work also has not carefully modeled variations of such influence among neighboring documents. Recognizing these modeling limitations, this paper introduces a novel Local Context‐Aware LDA Model (LC‐LDA), which is capable of observing a local context comprising a rich collection of documents that may directly or indirectly influence the topic distributions of a target document. The proposed model can also differentiate the respective influence of each document in the local context on the target document according to both structural and temporal relationships between the two documents. The proposed model is extensively evaluated through multiple document clustering and classification tasks conducted over several large‐scale document sets. Evaluation results clearly and consistently demonstrate the effectiveness and superiority of the new model with respect to several state‐of‐the‐art peer models.

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LPTA: A Probabilistic Model for Latent Periodic Topic Analysis

Cited by 16 publications

References 31 publications

Discovering Temporal Retweeting Patterns for Social Media Marketing Campaigns

Discovering Temporal Retweeting Patterns for Social Media Marketing Campaigns

An N-Gram Topic Model for Time-Stamped Documents

A local context‐aware LDA model for topic modeling in a document network

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