Text Classification For Authorship Attribution Analysis
This work addresses authorship attribution for literary texts, but it is incremental as it combines existing classifiers without introducing new methods.
The paper tackled authorship attribution by comparing fuzzy learning and SVM classifiers, finding SVM more accurate individually, and then combining them to achieve better accuracy than either alone.
Authorship attribution mainly deals with undecided authorship of literary texts. Authorship attribution is useful in resolving issues like uncertain authorship, recognize authorship of unknown texts, spot plagiarism so on. Statistical methods can be used to set apart the approach of an author numerically. The basic methodologies that are made use in computational stylometry are word length, sentence length, vocabulary affluence, frequencies etc. Each author has an inborn style of writing, which is particular to himself. Statistical quantitative techniques can be used to differentiate the approach of an author in a numerical way. The problem can be broken down into three sub problems as author identification, author characterization and similarity detection. The steps involved are pre-processing, extracting features, classification and author identification. For this different classifiers can be used. Here fuzzy learning classifier and SVM are used. After author identification the SVM was found to have more accuracy than Fuzzy classifier. Later combined the classifiers to obtain a better accuracy when compared to individual SVM and fuzzy classifier.