Algorithm comparison on a social platform

I used newspaper articles in my experiments until now. I decided to use texts which extracted from other platforms, so I collected texts from eksisozluk platform. Ekşisözlük is a kind of local Reddit. I tried to perform a comparison experiment by using Turkish gerunds as features.
Here my experiment components:

    Corpus: Eksisozluk dataset of 5 authors represented by nicknames, 100 texts for each author. Average word count is 461, 80% of the dataset is used as training data and 20% of the dataset is used as test data.
    Features: Features are Turkish gerunds. These words are derived from the verbs but used as nouns, adjectives, and adverbs in a sentence. I listed the most widely used verbs in Turkish, after that I derived gerunds by using gerund suffixes. Finally, I obtained 590 verbal nouns, 587 verbal adjectives and 916 verbal adverbs (with proper vowel versions).
    Algorithms: Algorithms are LinearSVM, Multi-Layer Perceptron (MLP), Naive Bayes (NB), k-Nearest Neighbor (kNN) and Decision Tree.

Now, the results are below.


The performance of SVM with gerund frequencies as features is not satisfied, it classified just 3 of 5 authors with correct matching minimum 12 of 20 test documents.


The performance of MLP with gerund features is slightly better than SVM. For example, it classified 4 of 5 authors with correct matching minimum 12 of 20 test documents.


The performance of NB is average and close to other results. For example, it classified 3 of 5 authors with correct matching minimum 12 of 20 test documents.

    Decision Tree

The performance of Decision tree is not enough, average F1-score is 0.39. It did not make satisfied correct matching.


The performance of kNN not enough but slightly better than decision tree, average F1-score is 0.44. It classified only one of 5 authors with correct matching 16 of 20 test documents.

As a result, NB, kNN and decision tree are not suitable algorithms for this approach. SVM and MLP performed better than other algorithms.


Popular stylometric features of Turkish author detection

I prepare a survey about author detection on Turkish for a while. I had gathered twelve studies, and then I examined them regarding preferred stylometric features and used algorithms. There are eight types of stylometric features; token-based, vocabulary richness, word frequency, word n-gram, character-based, character n-gram, part of speech and functional words.


The numbers on the Y axis refer that how many study use this feature. The most used feature is word frequency, the second is token-based feature.

On the other hand, there are eight most preferred algorithms in the Turkish author detection studies. These algorithms are Naive Bayesian, Neural Networks, SVM, Decision Tree, Random Forest, k-NN, k-Means and other (Gaussian classifier, Histogram, similarity based etc.)


As shown on the graph the most preferred algorithm is Naive Bayesian, the second used algorithm is SVM, and the third one is Random Forest.