Identification of Age and Gender on Twitter Using DenseNet and LSTM

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ID: 313203
2023
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Ranked #411 of 705 articles by views in Journal of Computing & Biomedical Informatics

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Abstract
This study presents a novel and automated analysis on social media platforms to distinguish between the gender and identify age group of users. The personality of a person can be determined through his or her social behavior. The proposed design employs dense deep learning framework to automatically identify the gender whenever users express their thoughts and comments on public forums. The system aims to provide gender and age group information to organizations to plan future needs of people and may focus on a particular gender or age group. By learning from the proposed sentiment model, it is observed that deep features are strongly associated with gender discrimination. Sentiment analysis is a technique that identifies emotions in a text and computes scores for the post to lsbel it as positive, negative, or neutral. Users’ messages or posts are identified into gender, age, or location, and a detailed assessment is provided to segment increasing population into respective groups and categories. In this work, the proposed gender detection model using sentiment analysis and LSTM (long short term memory) algorithm achieves 91% classification accuracy whereas age group assessment model performs at 85% accuracy level.
Reference Key
imported_1777059764_69ebc7b40cfe6 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mariyum Nizami
Journal Journal of Computing & Biomedical Informatics
Year 2023
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/199
Keywords Keywords not found

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