Tracking the evolution of social emotions with topic models

Zhu, Chen; Zhu, Hengshu; Ge, Yong; Chen, Enhong; Liu, Qi; Xu, Tong; Xiong, Hui

doi:10.1007/s10115-015-0865-0

Cited by 12 publications

(3 citation statements)

References 27 publications

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“…This work precedes the advent of powerful pretrained language models (PLMs). Dynamic topic models have been employed in social media for modeling the evolution of emotions and topics in subject-specific reviews and news corpora (He et al, 2014;Zhu et al, 2016). Although such work forms a strong foundation for dynamic representation modeling, temporal individual linguistic content spans across multiple unique topics unlike reviews and news documents that are heavily governed by aggregate topics.…”

Section: Related Workmentioning

confidence: 99%

Sequential Path Signature Networks for Personalised Longitudinal Language Modeling

Tseriotou,

Tsakalidis,

Foster

et al. 2023

Findings of the Association for Computational Linguistics: ACL 2023

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Longitudinal user modeling can provide a strong signal for various downstream tasks. Despite the rapid progress in representation learning, dynamic aspects of modelling individuals' language have only been sparsely addressed. We present a novel extension of neural sequential models using the notion of path signatures from rough path theory, which constitute graduated summaries of continuous paths and have the ability to capture non-linearities in trajectories. By combining path signatures of users' history with contextual neural representations and recursive neural networks we can produce compact time-sensitive user representations. Given the magnitude of mental health conditions with symptoms manifesting in language, we show the applicability of our approach on the task of identifying changes in individuals' mood by analysing their online textual content. By directly integrating signature transforms of users' history in the model architecture we jointly address the two most important aspects of the task, namely sequentiality and temporality. Our approach 1 achieves state-of-the-art performance on macro-average F1 score on the two available datasets for the task, outperforming or performing on-par with state-of-the-art models utilising only historical posts and even outperforming prior models which also have access to future posts of users.

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Section: Related Workmentioning

confidence: 99%

Sequential Path Signature Networks for Personalised Longitudinal Language Modeling

Tseriotou,

Tsakalidis,

Foster

et al. 2023

Findings of the Association for Computational Linguistics: ACL 2023

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“…Topic models have been successfully applied to problems in a variety of fields, such as marketing campaign (Liu et al 2014), emotion recognition (Zhu et al 2016a), biometrics (Wang et al 2011), genetics (Blei and Lafferty 2007), social media (Xu et al 2014) and mobile data mining (Farrahi and Gatica-Perez 2012;Zhu et al 2015).…”

Section: Topic Modelsmentioning

confidence: 99%

Measuring the Popularity of Job Skills in Recruitment Market: A Multi-Criteria Approach

Zhu

et al. 2018

AAAI

Self Cite

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To cope with the accelerating pace of technological changes, talents are urged to add and refresh their skills for staying in active and gainful employment. This raises a natural question: what are the right skills to learn? Indeed, it is a nontrivial task to measure the popularity of job skills due to the diversified criteria of jobs and the complicated connections within job skills. To that end, in this paper, we propose a data driven approach for modeling the popularity of job skills based on the analysis of large-scale recruitment data. Specifically, we first build a job skill network by exploring a large corpus of job postings. Then, we develop a novel Skill Popularity based Topic Model (SPTM) for modeling the generation of the skill network. In particular, SPTM can integrate different criteria of jobs (e.g., salary levels, company size) as well as the latent connections within skills, thus we can effectively rank the job skills based on their multi-faceted popularity. Extensive experiments on real-world recruitment data validate the effectiveness of SPTM for measuring the popularity of job skills, and also reveal some interesting rules, such as the popular job skills which lead to high-paid employment.

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“…Latent Dirichlet Allocation (LDA) [12] based latent variable models, have become one of the most powerful tools for mining textual data. However, most topic models [11,31,32] need a predefined parameter to indicate the number of topics, and thus fail to capture the variability of topics. To this end, the Hierarchical Dirichlet Processes (HDP) [28] is proposed as an infinity version of topic model, which can automatically learn the number of topics.…”

Section: Sequential Latent Variable Modelmentioning

confidence: 99%

Recruitment Market Trend Analysis with Sequential Latent Variable Models

Zhu

Xiong

et al. 2016

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Recruitment market analysis provides valuable understanding of industry-specific economic growth and plays an important role for both employers and job seekers. With the rapid development of online recruitment services, massive recruitment data have been accumulated and enable a new paradigm for recruitment market analysis. However, traditional methods for recruitment market analysis largely rely on the knowledge of domain experts and classic statistical models, which are usually too general to model large-scale dynamic recruitment data, and have difficulties to capture the fine-grained market trends. To this end, in this paper, we propose a new research paradigm for recruitment market analysis by leveraging unsupervised learning techniques for automatically discovering recruitment market trends based on large-scale recruitment data. Specifically, we develop a novel sequential latent variable model, named MTLVM, which is designed for capturing the sequential dependencies of corporate recruitment states and is able to automatically learn the latent recruitment topics within a Bayesian generative framework. In particular, to capture the variability of recruitment topics over time, we design hierarchical dirichlet processes for MTLVM. These processes allow to dynamically generate the evolving recruitment topics. Finally, we implement a prototype system to empirically evaluate our approach based on real-world recruitment data in China. Indeed, by visualizing the results from MTLVM, we can successfully reveal many interesting findings, such as the popularity of LBS related jobs reached the peak in the 2nd half of 2014, and decreased in 2015.Comment: 11 pages, 30 figure, SIGKDD 201

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Tracking the evolution of social emotions with topic models

Cited by 12 publications

References 27 publications

Sequential Path Signature Networks for Personalised Longitudinal Language Modeling

Sequential Path Signature Networks for Personalised Longitudinal Language Modeling

Measuring the Popularity of Job Skills in Recruitment Market: A Multi-Criteria Approach

Recruitment Market Trend Analysis with Sequential Latent Variable Models

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