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Our research utilizes diverse classifiers, comprising unsupervised learning approaches such as Valence Aware Dictionary and sEntiment Reasoner (VADER) and Text Blob, alongside Supervised Learning methods like Na\u00efve Bayes, which encompasses both the Bernoulli NB and Multinomial NB. Several methodologies have been utilized, including the Count Vectorizer, and the Term Frequency-Inverse Document Frequency model (TFIDF) Vectorizer. Subsequently, word embedding and bidirectional LSTM are executed, utilizing various embeddings such as the Long Short-Term Memory (LSTM) base model. 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