Utilizing Machine Learning for Natural Language Processing to Conduct Sentiment Analysis on Twitter Data in Multiple Languages

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Retailers, market analysts, and other users of the web are greatly influenced by user views. Arranging the unstructured data gathered from various social media networks correctly is necessary for doing relevant analysis. Emotional evaluation as a method for cross-lingual data classification has received considerable attention. Textual organization is a subfield of natural language processing, or NLP, that may be used to classify an individual's emotional or mental condition as positive, negative, beneficial, or detrimental, like a thumbs up or thumbs down, etc. A combination of sentiment analysis as well as deep learning techniques might be the key to solving this kind of problem. Deep learning models, which are capable of machine learning, are particularly useful for this. One of the most widely used deep learning architectures for analyzing sentiment in text is called Long Short Term Memory. These frameworks have potential applications in NLP. In this study, we provide algorithms to solve the problem of multilingual sentiment analysis, and we evaluate their precision factors to determine which one is the most effective.