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Prediction of Age, Sentiment, and Connectivity from Social Media Text

  • Thin Nguyen
  • Dinh Phung
  • Brett Adams
  • Svetha Venkatesh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6997)

Abstract

Social media corpora, including the textual output of blogs, forums, and messaging applications, provide fertile ground for linguistic analysis material diverse in topic and style, and at Web scale. We investigate manifest properties of textual messages, including latent topics, psycholinguistic features, and author mood, of a large corpus of blog posts, to analyze the impact of age, emotion, and social connectivity. These properties are found to be significantly different across the examined cohorts, which suggest discriminative features for a number of useful classification tasks. We build binary classifiers for old versus young bloggers, social versus solo bloggers, and happy versus sad posts with high performance. Analysis of discriminative features shows that age turns upon choice of topic, whereas sentiment orientation is evidenced by linguistic style. Good prediction is achieved for social connectivity using topic and linguistic features, leaving tagged mood a modest role in all classifications.

Keywords

Latent Dirichlet Allocation Discriminative Feature Social Connectivity Latent Topic Current Mood 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Thin Nguyen
    • 1
  • Dinh Phung
    • 1
  • Brett Adams
    • 1
  • Svetha Venkatesh
    • 1
  1. 1.Curtin UniversityAustralia

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