Personality Recognition on Social Media With Label Distribution Learning

Xue, Di; Zheng, Hong; Guo, Shize; Gao, Liang; Wu, Lifa; Zheng, Jinghua; Zhao, Nan

doi:10.1109/access.2017.2719018

Cited by 51 publications

(27 citation statements)

References 39 publications

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“…In addition to the explosive popularity of social media, online social networks have conducted numerous studies for personality recognition [2][3][4][5][6].Author Tommy Tandera [2] has developed a prediction system that can automatically predict user personality based on their activities on Facebook. This study utilizes the personality of Big Five.…”

Section: A Research Reviewmentioning

confidence: 99%

“…A neural network completely linked with one hidden layer is used for classification purposes. Author D. Xue [4] suggested a technique of microblog character recognition with a fresh machine learning paradigm called label distribution learning (LDL). One hundred and thirteen characteristics are obtained from the profiles and microblogs of 994 active Sina Weibo clients.…”

Section: A Research Reviewmentioning

confidence: 99%

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Personality Prediction from Social Networks text using Machine Learning

Bhamare*,

Kumar

2019

IJRTE

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Personality, a typical way of thinking, feeling, and behaviour. Personality embraces moods, attitudes and views and is expressed most obviously in relationships with others. It involves both intrinsic and acquired behavioural features that differentiate one individual from another and can be found in the relationships of people with the surroundings and with the social group. With the development of social networks, a broad variety of techn iques have been developed to identify user personalities based on their social activities and language usage practices. In terms of distinct machine learning algorithms, information sources and function sets, particular methods vary. Personality prediction has been an important research topic for describing user profiles and person not only in psychology but also in computer science. This paper presents a systematic survey of current work done of personality prediction from social networks. We also prepared a Comparison chart of existing techniques for personality prediction on the basis of relevant parameters. Based on this survey, we finally presented a few future research directions related to personality prediction.

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Section: A Research Reviewmentioning

confidence: 99%

Section: A Research Reviewmentioning

confidence: 99%

Personality Prediction from Social Networks text using Machine Learning

Bhamare*,

Kumar

2019

IJRTE

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“…Current methods, however, still fail to explicitly account for such biases, and can in fact even increase the demographic bias (Zhao et al, 2017). While it is possible to counter-act this bias, it requires our specific attention.…”

Section: Debiasing and Other Applicationsmentioning

confidence: 99%

“…As Chinese characters are harder to delimit than other languages, they utilized Jieba, a Chinese character tokenizer. Lastly, Xue et al (2017) focused on the use of Label Distribution Learning as an alternative to common machine learning algorithms while processing Chinese text. They extract information from posts from Sina Weibo users with TextMind, a Chinese language psychological analysis system similar to LIWC.…”

Section: Future Workmentioning

confidence: 99%

“…Demographic bias in the training data can severely distort the performance of our tools (Jørgensen et al, 2015;Hovy and Søgaard, 2015;Zhao et al, 2017), while accounting for demographic factors can actually improve performance in a variety of tasks Hovy, 2015;Lynn et al, 2017;Yang and Eisenstein, 2017;Benton et al, 2017). In order to move forward as a field, we will have to follow two strands of research: 1) we need to identify the specific demographic factors that do have an influence on NLP models (on bias and performance), and 2) based on this knowledge, we need to develop models that account for demographics to improve performance while preventing bias.…”

Section: Introductionmentioning

confidence: 99%

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Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media

2018

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The idea of organizing PEOPLES stemmed from two related observations, namely the availability of large amounts of spontaneous data covering a range of personal aspects and the fact that such aspects are usually studied in isolation. Social media users nowadays freely express what is on their mind at any moment in time, at any location, and about virtually anything. These large amounts of spontaneously produced texts open up a unique opportunity to learn more about such users, e.g., predicting demographic variables (age, gender), but also personality types, as well as emotions and opinion expressions. This observation is not new, of course, and this opportunity has largely been exploited in the recent years, with abundant works on sentiment analysis, emotion detection, and personality. However, such traits of human personality and behavior have indeed attracted a substantial amount of attention but have been mostly studied in isolation, often in different -but related -communities, such as NLP, CL, AI. Therefore, we thought that the time was ripe to bring these communities a step closer to study people's traits and expressions jointly and in their interplay on such large volumes of available data.The communities' response, with 25 received submissions coming from 11 different countries and going well beyond typical NLP topics, proves again this year that there is wide interest at this intersection, and we are happy to be able to provide a context for exchanging ideas.Following the reviewers's advice, 14 papers were selected for inclusion in the proceedings. They cover a wide range of topics related to the three main PEOPLES themes (personality, emotion and opinion), their interaction and the impact of their modeling on social aspects like well-being, political preferences, humor and language use.To further enrich this volume, we additionally invited our keynote speakers to submit position papers that accompany their talks, and are excited that both of our keynotes submitted excellent papers touching upon issues of making NLP models more demographically aware and how researchers from related fields such as demography can benefit from NLP techniques.We hope that this is just the second edition of what will become series of workshops bringing together researchers in Computational Linguistics, Natural Language Processing and Computational Social Science, who share an interest in personality, opinion and emotion detection, and especially in researching the intertwining of such traits and expressions.We would like to thank our program committee consisting of 33 researchers from a variety of backgrounds for their insightful and constructive reviews. Without their support, this workshop would not have been possible. In addition, we thank all authors for submitting papers and making PEOPLES a big success. Also thanks to our two invited speakers, Dirk Hovy and Letizia Mencarini (Bocconi University, Italy), for having accepted to come to the workshop and share their expertise and ideas on PEOPLES' topics. We thank NAACL for...

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Personality Extraction Through LinkedIn

Piedboeuf

Langlais

Bourg³

2019

Advances in Artificial Intelligence

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Personality extraction through social networks is a field that only recently started to capture the attention of researchers. The task consists in, starting with a corpus of user profiles on a particular social network, classifying their personalities correctly, according to a specific personality model as described in psychology. In this master thesis, three innovations to the domain are presented. Firstly, the collection of a corpus of LinkedIn users. Secondly, the extraction of the personality according to two personality models, DiSC and MBTI, the extraction with DiSC having never been done before. Lastly, the idea of going from one personality model to the other is explored, thus creating the possibility of having the results on two personality models with only one personality test.

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Personality Recognition on Social Media With Label Distribution Learning

Cited by 51 publications

References 39 publications

Personality Prediction from Social Networks text using Machine Learning

Personality Prediction from Social Networks text using Machine Learning

Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media

Personality Extraction Through LinkedIn

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