International Journal of Engineering and Technology

Text mining is in trend of research and development these days. A number of recent contributions in this domain are observed. Different social media text analysis techniques are used to find the trending topic. Both kinds of data mining techniques (i.e. supervised and unsupervised) are used in text mining. The proposed study is focused on the supervised learning approach. In the first step the dataset is prepared, thus different subjects or domain based data is extracted from twitter and according to these subjects the class labels are appended with the data. Further the dataset is split into two sets i.e. the training data and the testing data. In the next step the data is pre-processed where stop words and special characters are removed from the dataset. This dataset is used with the three different classification algorithms KNN (k-nearest neighbor), SVM (support vector machine) and a combination of both SVM and KNN. Hybrid approach is applied to build the classification model and it is implemented on test dataset to discover the twitter subjects. The result of the proposed work is compared with traditional KNN and SVM algorithm. According to the results obtained, the proposed hybrid technique provides more precise and better results as compared to other conventional classifiers.