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In a world with 7,000+ languages, Papua New Guinea leads with 839. Indonesia has 706, China 301, and Arabic features numerous variations. Understanding emotional nuances in diverse languages poses a challenge, especially for non-native speakers, tourists, and influencers. Social media has emerged as a vital information source for tourism, influencing, and vlogging. This study aims to outline the use of sentiment analysis in social media platforms. It provides valuable insights for travelers, vloggers, foreigners, and influencers, aiding them in making informed decisions and ensuring safety and satisfaction. It compares emotion identification across nine languages and evaluates SVM, LSTM, BiLSTM models with two word embeddings. The goal is to classify tweets into Positive, Neutral, and Negative emotions. Our Custom Model consistently outperforms existing works. It achieves an impressive 90.01% accuracy with a dataset exceeding 270,399 sentences in multiple languages.
Hasan et al. (Thu,) studied this question.