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TextAge: A Curated and Diverse Text Dataset for Age Classification

Shravan Cheekati, Mridul Gupta, Vibha Raghu, Pranav Raj

TL;DR

TextAge is a curated text dataset that maps sentences to the age and age group of the producer, as well as an underage (under 13) label, that offers a valuable resource for studying age-related language patterns and developing age-sensitive language models.

Abstract

Age-related language patterns play a crucial role in understanding linguistic differences and developing age-appropriate communication strategies. However, the lack of comprehensive and diverse datasets has hindered the progress of research in this area. To address this issue, we present TextAge, a curated text dataset that maps sentences to the age and age group of the producer, as well as an underage (under 13) label. TextAge covers a wide range of ages and includes both spoken and written data from various sources such as CHILDES, Meta, Poki Poems-by-kids, JUSThink, and the TV show "Survivor." The dataset undergoes extensive cleaning and preprocessing to ensure data quality and consistency. We demonstrate the utility of TextAge through two applications: Underage Detection and Generational Classification. For Underage Detection, we train a Naive Bayes classifier, fine-tuned RoBERTa, and XLNet models to differentiate between language patterns of minors and young-adults and over. For Generational Classification, the models classify language patterns into different age groups (kids, teens, twenties, etc.). The models excel at classifying the "kids" group but struggle with older age groups, particularly "fifties," "sixties," and "seventies," likely due to limited data samples and less pronounced linguistic differences. TextAge offers a valuable resource for studying age-related language patterns and developing age-sensitive language models. The dataset's diverse composition and the promising results of the classification tasks highlight its potential for various applications, such as content moderation, targeted advertising, and age-appropriate communication. Future work aims to expand the dataset further and explore advanced modeling techniques to improve performance on older age groups.

TextAge: A Curated and Diverse Text Dataset for Age Classification

TL;DR

TextAge is a curated text dataset that maps sentences to the age and age group of the producer, as well as an underage (under 13) label, that offers a valuable resource for studying age-related language patterns and developing age-sensitive language models.

Abstract

Age-related language patterns play a crucial role in understanding linguistic differences and developing age-appropriate communication strategies. However, the lack of comprehensive and diverse datasets has hindered the progress of research in this area. To address this issue, we present TextAge, a curated text dataset that maps sentences to the age and age group of the producer, as well as an underage (under 13) label. TextAge covers a wide range of ages and includes both spoken and written data from various sources such as CHILDES, Meta, Poki Poems-by-kids, JUSThink, and the TV show "Survivor." The dataset undergoes extensive cleaning and preprocessing to ensure data quality and consistency. We demonstrate the utility of TextAge through two applications: Underage Detection and Generational Classification. For Underage Detection, we train a Naive Bayes classifier, fine-tuned RoBERTa, and XLNet models to differentiate between language patterns of minors and young-adults and over. For Generational Classification, the models classify language patterns into different age groups (kids, teens, twenties, etc.). The models excel at classifying the "kids" group but struggle with older age groups, particularly "fifties," "sixties," and "seventies," likely due to limited data samples and less pronounced linguistic differences. TextAge offers a valuable resource for studying age-related language patterns and developing age-sensitive language models. The dataset's diverse composition and the promising results of the classification tasks highlight its potential for various applications, such as content moderation, targeted advertising, and age-appropriate communication. Future work aims to expand the dataset further and explore advanced modeling techniques to improve performance on older age groups.
Paper Structure (13 sections, 3 figures, 3 tables)

This paper contains 13 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Methods
  4. Data Collection
  5. Data Cleaning
  6. Data Exploration
  7. Applications
  8. Underage/Of-Age Detection
  9. Results & Analysis
  10. Generation Classification
  11. Results & Analysis
  12. Conclusion
  13. Appendix

Figures (3)

  • Figure 1: Distribution by Age Group of our Final Dataset. Highlights less data for teens and older age groups.
  • Figure 2: Visualizes how each source contributes to our final dataset of 608082 rows. Count represents number of entries per source before cleaning.
  • Figure 3: XLNet and RoBERTa models performance metrics on binary and multiclass tasks.