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Experimental Study of Gender and Language Variety Identification in Social Media

  • Vineetha Rebecca ChackoEmail author
  • M. Anand Kumar
  • K. P. Soman
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 750)

Abstract

Social media has evolved to be a crucial part of life today for everyone. With such a global population communicating with each other, comes the accumulation of large amounts of social media data. This data can be categorized as “Big Data”, owing to its large quantity. It contains valuable information in the form of the demographics of authors on online platforms; the analysis of which is required in certain scenarios to maintain decorum in the online community. Here, we have analyzed Twitter data, which is the training data of the PAN@CLEF 2017 shared task contest, to identify the gender, as well as the language variety of the author. It is available in four different languages, namely, English, Spanish, Portuguese, and Arabic. Both Document-Term Matrix (DTM) and Term Frequency-Inverse Document Frequency (TF-IDF) have been used for text representation. The classifiers used are SVM, AdaBoost, Decision Tree, and Random Forest.

Keywords

Document Term Matrix Term Frequency-Inverse Document Frequency n-grams Support vector machines AdaBoost Random Forest Decision Tree Author Profiling 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Vineetha Rebecca Chacko
    • 1
    Email author
  • M. Anand Kumar
    • 1
  • K. P. Soman
    • 1
  1. 1.Centre for Computational Engineering and Networking (CEN), Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia

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