Geographical Latent Variable Models for Microblog Retrieval

Kotov, Alexander; Rakesh, Vineeth; Agichtein, Eugene; Reddy, Chandan K.

doi:10.1007/978-3-319-16354-3_70

Cited by 7 publications

(3 citation statements)

References 18 publications

(20 reference statements)

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“…In particular, PLDA and PLDP introduce an additional layer of latent variables that determine associations of each word with a document tag and topic. Topic models incorporating the locations of Twitter users extracted from their profiles have been shown to improve microblog retrieval in [12] and [13]. Although these models shed some light on how to incorporate useful metadata into topic modeling process, none of them has been applied to aspect-based sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

Parametric and Non-parametric User-aware Sentiment Topic Models

Yang

Kotov

Mohan

et al. 2015

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

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The popularity of Web 2.0 has resulted in a large number of publicly available online consumer reviews created by a demographically diverse user base. Information about the authors of these reviews, such as age, gender and location, provided by many online consumer review platforms may allow companies to better understand the preferences of different market segments and improve their product design, manufacturing processes and marketing campaigns accordingly. However, previous work in sentiment analysis has largely ignored these additional user meta-data. To address this deficiency, in this paper, we propose parametric and non-parametric User-aware Sentiment Topic Models (USTM) that incorporate demographic information of review authors into topic modeling process in order to discover associations between market segments, topical aspects and sentiments. Qualitative examination of the topics discovered using USTM framework in the two datasets collected from popular online consumer review platforms as well as quantitative evaluation of the methods utilizing those topics for the tasks of review sentiment classification and user attribute prediction both indicate the utility of accounting for demographic information of review authors in opinion mining.

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Section: Related Workmentioning

confidence: 99%

Parametric and Non-parametric User-aware Sentiment Topic Models

Yang

Kotov

Mohan

et al. 2015

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…The authors of [34,10,15] adopt a different methodology of suggesting location by segmenting geographical areas into sub-regions based on the characteristics of POIs. Topic models incorporating geographical and social information have also been shown to be effective for other tasks, such as opinion mining [30] and social media information retrieval [11,13,12].…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Social Sequential Model for Tour Recommendation

Rakesh

Jadhav

Kotov

et al. 2017

Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

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The pervasive growth of location-based services such as Foursquare and Yelp has enabled researchers to incorporate better personalization into recommendation models by leveraging the geo-temporal breadcrumbs left by a plethora of travelers. In this paper, we explore Travel path recommendation, which is one of the applications of intelligent urban navigation that aims in recommending sequence of point of interest (POIs) to tourists. Currently, travelers rely on a tedious and time-consuming process of searching the web, browsing through websites such as Trip Advisor, and reading travel blogs to compile an itinerary. On the other hand, people who do not plan ahead of their trip find it extremely difficult to do this in real-time since there are no automated systems that can provide personalized itinerary for travelers. To tackle this problem, we propose a tour recommendation model that uses a probabilistic generative framework to incorporate user's categorical preference, influence from their social circle, the dynamic travel transitions (or patterns) and the popularity of venues to recommend sequence of POIs for tourists. Through comprehensive experiments over a rich dataset of travel patterns from Foursquare, we show that our model is capable of outperforming the state-of-the-art probabilistic tour recommendation model by providing contextual and meaningful recommendation for travelers.

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“…Several works in topic models utilize nontextual data such as prices (Iwata & Sawada, 2013), authors' demographic information (Yang, Kotov, Mohan, & Lu, 2015), and geographical locations (Kotov, Rakesh, Agichtein, & Reddy, 2015;Kotov, Wang, & Agichtein, 2013). Other works also proposed the inclusion of hashtags in topic models.…”

Section: Nontextual Data In Topic Modelsmentioning

confidence: 99%

Improving interpretations of topic modeling in microblogs

Alkhodair

Fung

Rahman

et al. 2017

Asso for Info Science & Tech

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Topic models were proposed to detect the underlying semantic structure of large collections of text documents to facilitate the process of browsing and accessing documents with similar ideas and topics. Applying topic models to short text documents to extract meaningful topics is challenging. The problem becomes even more complicated when dealing with short and noisy micro‐posts in Twitter that are about one general topic. In such a case, the goal of applying topic models is to extract subtopics. This results in topics represented by similar sets of keywords, which in turn makes the process of topic interpretation more confusing. In this paper we propose a new method that incorporates Twitter‐LDA, WordNet, and hashtags to enhance the keyword labels that represent each topic. We emphasize the importance of different keywords to different topics based on the semantic relationships and the co‐occurrences of keywords in hashtags. We also propose a method to find the best number of topics to represent the text document collection. Experiments on two real‐life Twitter datasets on fashion suggest that our method performs better than the original Twitter‐LDA in terms of perplexity, topic coherence, and the quality of keywords for topic labeling.

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Geographical Latent Variable Models for Microblog Retrieval

Cited by 7 publications

References 18 publications

Parametric and Non-parametric User-aware Sentiment Topic Models

Parametric and Non-parametric User-aware Sentiment Topic Models

Probabilistic Social Sequential Model for Tour Recommendation

Improving interpretations of topic modeling in microblogs

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