  Forums play an important role in providing a platform for community
interaction. The introduction of irrelevant content or spam by individuals for
commercial and social gains tends to degrade the professional experience
presented to the forum users. Automated moderation of the relevancy of posted
content is desired. Machine learning is used for text classification and finds
applications in spam email detection, fraudulent transaction detection etc. The
balance of classes in training data is essential in the case of classification
algorithms to make the learning efficient and accurate. However, in the case of
forums, the spam content is sparse compared to the relevant content giving rise
to a bias towards the latter while training. A model trained on such biased
data will fail to classify a spam sample. An approach based on Synthetic
Minority Over-sampling Technique(SMOTE) is presented in this paper to tackle
imbalanced training data. It involves synthetically creating new minority class
samples from the existing ones until balance in data is achieved. The enhanced
data is then passed through various classifiers for which the performance is
recorded. The results were analyzed on the data of forums of Spoken Tutorial,
IIT Bombay over standard performance metrics and revealed that models trained
after Synthetic Minority oversampling outperform the ones trained on imbalanced
data by substantial margins. An empirical comparison of the results obtained by
both SMOTE and without SMOTE for various supervised classification algorithms
have been presented in this paper. Synthetic oversampling proves to be a
critical technique for achieving uniform class distribution which in turn
yields commendable results in text classification. The presented approach can
be further extended to content categorization on educational websites thus
helping to improve the overall digital learning experience.
