{"url":"/task/sentiment-analysis","name":"Sentiment Analysis","slug":"sentiment-analysis","description_markdown":"**Sentiment Analysis** is the task of classifying the polarity of a given text. For instance, a text-based tweet can be categorized into either \"positive\", \"negative\", or \"neutral\". Given the text and accompanying labels, a model can be trained to predict the correct sentiment. \r\n\r\n**Sentiment Analysis** techniques can be categorized into machine learning approaches, lexicon-based approaches, and even hybrid methods. Some subcategories of research in sentiment analysis include: multimodal sentiment analysis, aspect-based sentiment analysis, fine-grained opinion analysis, language specific sentiment analysis.\r\n\r\nMore recently, deep learning techniques, such as RoBERTa and T5, are used to train high-performing sentiment classifiers that are evaluated using metrics like F1, recall, and precision. 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