Twitter user profiling based on text and community mining for market analysis

Ikeda, Kazushi; Hattori, Gen; Ono, Chihiro; Asoh, Hideki; Higashino, Teruo

doi:10.1016/j.knosys.2013.06.020

Cited by 124 publications

(58 citation statements)

References 19 publications

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“…For most recommender systems, user profile modeling mines users' preferences for personalized search from users' histories or similar users' contents [1,2,13]. By extracting a vector representation of the words, Boratto [14] proposed a novel method to model user profile and detected segments of users.…”

Section: User Profilesmentioning

confidence: 99%

“…Recently, socialized recommendation has become one of the most popular means of recommendations in various recommender systems that have been applied in the fields of E-commerce, social media platforms, web search engines, and so on [1]. In socialized recommender systems, mining socialized relationships is critical for pushing or sharing interesting people and things with target users.…”

Section: Introductionmentioning

confidence: 99%

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Personalized Recommendations Based on Sentimental Interest Community Detection

Zheng

Wang

2018

Scientific Programming

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Communities have become a popular platform of mining interests for recommender systems. The semantics of topics reflect users' implicit interests. Sentiments on topics imply users' sentimental tendency. People with common sentiments can form resonant communities of interest. In this paper, a resonant sentimental interest community-based recommendation model is proposed to improve the accuracy performance of recommender systems. First, we learn the weighted semantics vector and sentiment vector to model semantic and sentimental user profiles. Then, by combining semantic and sentimental factors, resonance relationship is computed to evaluate the resonance relationship of users. Finally, based on resonance relationships, resonant community is detected to discover a resonance group to make personalized recommendations. Experimental results show that the proposed model is more effective in finding semantics-related sentimental interests than traditional methods.

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Section: User Profilesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Personalized Recommendations Based on Sentimental Interest Community Detection

Zheng

Wang

2018

Scientific Programming

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“…Ulges et al (2012) detected TV viewers' gender and age via content-based concept detection. Ikeda et al (2013) and Sakaki et al (2014) used methods that incorporate information. Ikeda et al (2013) proposed a hybrid-based method using both text and community membership.…”

Section: Related Workmentioning

confidence: 99%

“…Ikeda et al (2013) and Sakaki et al (2014) used methods that incorporate information. Ikeda et al (2013) proposed a hybrid-based method using both text and community membership. Sakaki et al (2014) proposed a hybrid-based method using a combination of text and images, which builds a meta-classifier using the probability score out- put from text and image classifiers as input.…”

Section: Related Workmentioning

confidence: 99%

A Weighted Combination of Text and Image Classifiers for User Gender Inference

Taniguchi

Sakaki

Shigenaka

et al. 2015

Proceedings of the Fourth Workshop on Vision and Language

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Demographic attribute inference of social networking service (SNS) users is a valuable application for marketing and for targeting advertisements. Several studies have examined Twitter-user gender inference in natural language processing, image recognition, and other research domains. Reportedly, a combined approach using text data and image data outperforms an individual data approach. This paper presents a proposal of a novel hybrid approach. A salient benefit of our system is that features provided from a text classifier and from an image classifier are combined appropriately to infer male or female gender using logistic regression. The experimentally obtained results demonstrate that our approach markedly improves an existing combination-based method.

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“…However, the expansion of features A few reports in the literature describe studies of systems that infer the SNS user gender with information aside from the text. Ikeda et al (2013) leverages the accuracy of profile inference based on a text feature classifier by combining user cluster information. According to their study, the accuracy of classification that deals only with the user cluster is lower than that of the text classifier.…”

Section: Prior Workmentioning

confidence: 99%

Twitter User Gender Inference Using Combined Analysis of Text and Image Processing

Sakaki¹,

Miura²,

Ma³

et al. 2014

Proceedings of the Third Workshop on Vision and Language

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Profile inference of SNS users is valuable for marketing, target advertisement, and opinion polls. Several studies examining profile inference have been reported to date. Although information of various types is included in SNS, most such studies only use text information. It is expected that incorporating information of other types into text classifiers can provide more accurate profile inference. As described in this paper, we propose combined method of text processing and image processing to improve gender inference accuracy. By applying the simple formula to combine two results derived from a text processor and an image processor, significantly increased accuracy was confirmed.

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Twitter user profiling based on text and community mining for market analysis

Cited by 124 publications

References 19 publications

Personalized Recommendations Based on Sentimental Interest Community Detection

Personalized Recommendations Based on Sentimental Interest Community Detection

A Weighted Combination of Text and Image Classifiers for User Gender Inference

Twitter User Gender Inference Using Combined Analysis of Text and Image Processing

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