A Domain Specific ESA Inspired Approach for Document Semantic Description

Mazzola, Luca; Siegfried, Patrick; Waldis, Andreas; Kaufmann, Michael; Denzler, Alexander

doi:10.1109/is.2018.8710507

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Table 1 is a comparison of different Topic Modeling Methods that shows a summary of the concept and advantages, and disadvantages of five topic modeling approaches (Hornick, 2017;Gabrilovich & Markovitch, 2006;Mazzola et al, 2018;Řehůřek, 2019;Rehurek & Sojka, 2010;Shi et al, 2018). As shown in Table 1, ESA and LSA are tightly connected, and in principle, ESA cannot outperform the peak performance of LSA.…”

Section: Topic Modelingmentioning

confidence: 99%

A Topic Modeling and Sentiment Analysis Model for Detection and Visualization of Themes in Literary Texts

Chu¹,

Keikhosrokiani²,

Asl³

2022

JST

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Despite the growing emergence of new computer analytic software programs, the adoption and application of computer-based data mining and processing methods remain sparse in literary studies and analyses. This study proposes a text analytics lifecycle to detect and visualize the prevailing themes in a corpus of literary texts. Two objectives are to be pursued: First, the study seeks to apply a Topic Modeling approach with selected algorithms of LDA, LSI, NMF, and HDP that can effectively detect the recurring topics about the major themes developed in the dataset. Second, the project aims to apply a Sentiment Analysis model that can analyze the polarity of writers’ discourse on the detected thematic topics with the algorithms of Vader and TextBlob. The implementation of Topic Modeling has detected six thematic topics of sex, family, revolution, imprisonment, intellectual, and death. The adoption of the Sentiment Analysis model also revealed that the feelings attached to all the identified themes are largely negative sentiments expressed towards socio-political issues.

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Section: Topic Modelingmentioning

confidence: 99%

A Topic Modeling and Sentiment Analysis Model for Detection and Visualization of Themes in Literary Texts

Chu¹,

Keikhosrokiani²,

Asl³

2022

JST

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“…We expect to be able to compute the matching between the applicants data and jobs profiles in distributed settings. The matching algorithm will be based on natural language processing (NLP) and document semantic similarity estimation [9], and will benefit also from some AI-based concepts identification improvement for multi-grams [15]. This approach can support fairness by construction and bias-free results, at least from individual person prejudices.…”

Section: Solution Sketchmentioning

confidence: 99%

Towards a Trusted Support Platform for the Job Placement Task

Dubovitskaya

Mazzola

Denzler

2020

Euro-Par 2019: Parallel Processing Workshops

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HR tech is a new trend in the hiring process, still facing some inefficiencies and limits. This paper sketches a high level architecture of a trusted support platform for the job placement task. Our design is based on a multifaceted analysis of current practices and requirements from technical, legal, and social perspectives. Relying on the properties of intelligent data analysis approaches, blockchain technology, and distributed identity management, this solution will enable optimisation, compliance, and improvements of the candidate selection process for job openings. The outcomes of the current work will be further applied for the identification of data formats and specific technologies to be used for the implementation. Practical use-cases are currently under development with industry partners.

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A Domain Specific ESA Inspired Approach for Document Semantic Description

Cited by 2 publications

References 8 publications

A Topic Modeling and Sentiment Analysis Model for Detection and Visualization of Themes in Literary Texts

A Topic Modeling and Sentiment Analysis Model for Detection and Visualization of Themes in Literary Texts

Towards a Trusted Support Platform for the Job Placement Task

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