Author profiling for age and gender using combinations of features of various types

Iqra Ameer, Grigori Sidorov, Rao Muhammad Adeel Nawab

    Research output: Contribution to journalArticlepeer-review

    3 Scopus citations

    Abstract

    The process of automatic identification of an authors demographic traits like gender, age, native language, geographical location, personality type and others from his/her written text is termed as author profiling (AP). Currently, it has engaged the research community due to its promising uses in security, marketing, forensic, bogus account identification on public networks. A variety of benchmark corpora (English text) released by PAN shared task is used to perform our experiments. This study presents a Content-based approach for detection of authors traits (age group and gender) for same-genre author profiles. In our proposed method, we used a different set of features including syntactic n-grams of part-of-speech tags, traditional n-grams of part-of-speech tags, the combination of word n-grams and combination of character n-grams. We tried a range of classifier for several profile sizes. We used the word uni-grams and character tri-grams as our baseline approaches.We achieved best accuracy of 0.496 and 0.734 for both traits, i.e., age group and gender respectively, by applying the combination of word n-grams of various sizes. Experimental results signify that the combination of word n-grams can produce good results on benchmark corpora.

    Original languageEnglish
    Pages (from-to)4833-4843
    Number of pages11
    JournalJournal of Intelligent and Fuzzy Systems
    Volume36
    Issue number5
    DOIs
    StatePublished - 1 Jan 2019

    Keywords

    • Author profiling
    • Machine learning
    • Part-of-epeech
    • Syntactic n-grams
    • Traditional n-grams

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