A Comparative Analysis of Social Network Pages by Interests of Their Followers
Table 2: Interclass classification: F̄1-score. F - football, R - rock music, V - vegetarianism, T - Twitter, Vk - Vkontakte, En - English, Ru - Russian, SVM - Support Vector Machine, lin. - linear kernel, pol. - polynomial kernel, rad. - Radial Basis Function kernel, sig. - sigmoid kernel, Neur. - Neural Network, NB - Naive Bayes, Bern. - Bernoulli, Mult. - Multinomial, Gaus. - Gaussian, LR - Logistic Regression, DT - Decision Trees, K-N - K-Neighbours. ↑ marks cases where normalization was more effective compared to plain frequencies.
['[EMPTY]', 'F', 'Vk Ru R', 'V', 'F', 'T Ru R', 'V', 'F', 'T En R', 'V']
Classification algorithms that we chose for the survey are often met in NLP tasks like spam detection, sentiment analysis and the like: Naive Bayes, Support Vector Machine, Neural Network etc. In every new test, train and test sets were randomly created anew. The best performing algorithm is Linear Regression with Bernoulli model. The sum of its F̄1-scores equals 8.976. The second best score (8.95) belongs to the Neural Network (lbfgs). Multinomial Naive Bayes has the third best score (8.938) which is the same in all the three models. Hence, we can assume that in our research this classifier is most insensitive to the model type, although intitially it was designed for word frequencies. But for Multinomial NB, Bernoulli models take the first 8 places in the ranking table. All things considered, we believe that Bernoulli models are the best solution for the tested linguistic model of interest classification. We will now try to analyze differences in classification of the three datasets according to the MaI, the language of user comminucation and the network where the texts were posted. That leaves us with SVM (linear), Bernoulli NB, Linear Regression, Decision Trees with plain frequencies, and SVM (polynomial, sigmoid, RBF), and Neural Networks with normalized frequencies. Multinomial and Gaussian NB have the same result in the both models.