As companies are becoming increasingly data-driven, a Machine Learning technique called ‘Sentiment Analysis’ is gaining immense popularity day by day. It analyses the digital data/text through Natural Language Processing (NLP) to find the polarity (positive, negative, neutral), feelings, and emotions (angry, happy, sad, etc.) expressed in the text.

Since Twitter is one of the most comprehensive sources of live, public conversation worldwide, business firms, political groups, etc. are interested in performing ‘Sentiment Analysis’ of tweets to understand the emotions/opinions of the target market or for studying competitors’ market. Although they are ready to use programs for the purpose but to achieve predictions with a high level of accuracy, specific to particular criteria and domains, the best way is to create a customized Twitter Sentiment Analysis Python model or program.

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