Soft Voting Ensemble with N-GRAM Vectorization for Accurate News Classification on Twitter Data
Keywords:
Ensemble Learning, N-gram Vectorization, Social Media Analytics, News Classification, Text Classification TechniquesAbstract
With the exponential growth in the social media platform, the need for effective tools to accurately classify tweets, according to their news content has become very crucial. This work presents a comprehensive comparison of text classification techniques with various N-gram and vectorization techniques. Using the CybAttT dataset, our methodology employs text preprocessing techniques and then follow the data towards modelling. Six various Machine learning models are used on two vectorizing techniques namely TF-IDF and count vectorization. Following towards efficacy, these baseline six models are further again developed on soft voting ensemble model to leverage the strengths of each individual classifier. And the performance of each model was evaluated based on accuracy, precision, recall, and F1-score performance metrics. The results are rigorously compared on various N-gram configurations. From the experimental results, the soft voting ensemble model achieved an accuracy of 97.02% and 96.56% for count vectorization and TF-IDF for default n-gram. The comparison is also observed on bigram and trigram and out of all models, the ensemble model scores are superior to other machine learning models. These finding reveals that the proposed ensemble model advances text classification dynamics and also proposes a robust framework for researchers and practitioners focusing on social media analytics.
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