Matching and Ranking with Hidden Topics towards Online Contextual Advertising

Le, Dieu-Thu; Nguyen, Cam-Tu; Ha, Quang-Thuy; Phan, Xuan-Hieu; Horiguchi, Susumu

doi:10.1109/wiiat.2008.180

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…The most popular topic modeling technique is Latent Dirichlet Allocation (LDA), which is a generative probability distribution model for finding latent meaningful topics in the literature literature. 21,22 Most of the data used in topic modeling consists of various types of unstructured text data, such as websites and online advertising, 23 social-media publishing, and online product reviews. 24 There are other data, such as images, 25 purchase records, 26 mobile app usage history, 27 and traces of Internet search.…”

Section: Literature Review and Hypothesesmentioning

confidence: 99%

Using text analytics to measure an effect of topics and sentiments on social-media engagement: Focusing on Facebook fan page of Toyota

Chun

Leem

Suh

2021

International Journal of Engineering Business Management

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In this study we investigate whether Facebook fan-page posting types and topics have a significant effect on engagement. More specifically, the media type and content theme of posting on Facebook are examined to see whether or not there was a difference between content topics. In order to achieve this goal, we set hypotheses as follows: (1) the media types of posting have a significant effect on engagement; (2) the topics and sentiment polarity of posting have a significant effect on engagement. We tested these hypotheses using research procedures as follows: (1) collection and preprocessing of social-media data, including posting types, comments, and reactions on Facebook fan pages, (2) topic modeling of fan-page postings using R and SAS, (3) testing hypotheses using a negative binomial regression model, and (4) implications and insights for social-media marketing. Topic modeling applying to textual data and sentiment analysis were conducted. After that, in order to find the factors to affect the number of Facebook fan-page engagements, the negative binomial regression model including post type, topic, sentiment, reactions of “love,” “haha,” and their interaction as exploratory variables was considered. Finally, the results show that post type is the most influential factor to affect social-media engagement, and content topics, sentiments of posts and comments also have significant effects on it.

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Section: Literature Review and Hypothesesmentioning

confidence: 99%

Using text analytics to measure an effect of topics and sentiments on social-media engagement: Focusing on Facebook fan page of Toyota

Chun

Leem

Suh

2021

International Journal of Engineering Business Management

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Metadata Enrichment via Topic Models for Author Name Disambiguation

Bernardi

2011

Advanced Language Technologies for Digital Libraries

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Topic modeling in marketing: recent advances and research opportunities

Reisenbichler

Reutterer

2018

J Bus Econ

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Using a probabilistic approach for exploring latent patterns in high-dimensional co-occurrence data, topic models offer researchers a flexible and open framework for soft-clustering large data sets. In recent years, there has been a growing interest among marketing scholars and practitioners to adopt topic models in various marketing application domains. However, to this date, there is no comprehensive overview of this rapidly evolving field. By analyzing a set of 61 published papers along with conceptual contributions, we systematically review this highly heterogeneous area of research. In doing so, we characterize extant contributions employing topic models in marketing along the dimensions data structures and retrieval of input data, implementation and extensions of basic topic models, and model performance evaluation. Our findings confirm that there is considerable progress done in various marketing sub-areas. However, there is still scope for promising future research, in particular with respect to integrating multiple, dynamic data sources, including time-varying covariates and the combination of exploratory topic models with powerful predictive marketing models.

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Matching and Ranking with Hidden Topics towards Online Contextual Advertising

Cited by 5 publications

References 6 publications

Using text analytics to measure an effect of topics and sentiments on social-media engagement: Focusing on Facebook fan page of Toyota

Using text analytics to measure an effect of topics and sentiments on social-media engagement: Focusing on Facebook fan page of Toyota

Metadata Enrichment via Topic Models for Author Name Disambiguation

Topic modeling in marketing: recent advances and research opportunities

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