Optimization of Language Models by Word Computing

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


Word computation is a type of sentiment analysis that requires the identification not only of linguistic features, but also of the correlations of these features. There has been a great deal of research in this area. In order to understand linguistic representations and their applications in various domains of analysis, various factors such as demographics, emotions, and gender are taken into account in a transactional context. In this paper, we focus on those factors that can be extracted from existing data using natural language processing. We find that the most successful personality trait prediction models rely heavily on NLP techniques. To automate this process, researchers around the world have used a variety of machine learning and deep learning techniques. Different combinations of factors have led to different research results. We have conducted a comparative analysis of these experiments in the hope of determining the future course of action.

Original languageEnglish
Title of host publicationICGSP 2022 - 2022 6th International Conference on Graphics and Signal Processing
PublisherAssociation for Computing Machinery
Number of pages5
ISBN (Electronic)9781450396370
Publication statusPublished - 1 Jul 2022
Event6th International Conference on Graphics and Signal Processing, ICGSP 2022 - Chiba, Japan
Duration: 1 Jul 20223 Jul 2022

Publication series

NameACM International Conference Proceeding Series


Conference6th International Conference on Graphics and Signal Processing, ICGSP 2022


  • Context Vector
  • DropConnect
  • Language Model
  • NLP
  • Word Computing


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