Abstract
Twitter is a popular platform for sharing our perspective with the world and perceiving the expert opinions of the popular figures on day-to-day affairs. Politicians also find an outlet here for their campaigns, thereby reaching out to a vast audience online. In this research, we focus on identifying and classifying the subtleties of such political tweets that aim to influence the crowd. However, the noise in the dataset concerning linguistic anomalies makes it challenging to apply the direct classification methods. We begin by preprocessing the raw tweets to tackle grammatical and semantic issues. Further, natural language processing (NLP) tools such as Word2Vec that help in preserving semantic and syntactical relationships are incorporated. The classification accuracy is affected by this technique because grammatical structures distort with Word2Vec. Bigram count of special tokens is added to the resulting set of features to solve this problem. A Receiver Operating Characteristic (ROC) curve is used to measure the accuracy by selecting a different set of features, once using a Naïve Bayes classifier and once using random forest.
All authors have contributed equally
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2017 Spanish billion-word corpus and embeddings. URL https://crscardellino.github.io/SBWCE/.
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Varma, S.H., Harsha, Y.V.S. (2022). Political Polarity Classification Using NLP. In: Roy, S., Sinwar, D., Perumal, T., Slowik, A., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision . Advances in Intelligent Systems and Computing, vol 1424. Springer, Singapore. https://doi.org/10.1007/978-981-19-0475-2_3
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DOI: https://doi.org/10.1007/978-981-19-0475-2_3
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