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A sentiment analyzer learns about various sentiments behind a content through machine learning and predicts the same using AI. By creating an ML system that would analyze the sentiment behind texts, or a post, it might become so a lot easier for organizations to know and understand their consumer behavior better. Twitter data is taken into account as an ultimate entry point for beginners to practice sentiment analysis machine learning problems. Using Twitter datasets, one can get a charismatic combination of tweet contents and other related metadata such as hashtags, location, retweets, users, and many more which pave way for insightful analysis. The foremost problem that you can start working on as a beginner is to build a model to classify users’ profile photos as sad happy or neutral.
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A sentiment analyzer learns about various sentiments behind a content through machine learning and predicts the same using AI. By creating an ML system that would analyze the sentiment behind texts, or a post, it might become so a lot easier for organizations to know and understand their consumer behavior better. Twitter data is taken into account as an ultimate entry point for beginners to practice sentiment analysis machine learning problems. Using Twitter datasets, one can get a charismatic combination of tweet contents and other related metadata such as hashtags, location, retweets, users, and many more which pave way for insightful analysis. The foremost problem that you can start working on as a beginner is to build a model to classify users’ profile photos as sad happy or neutral.
The text was updated successfully, but these errors were encountered: