4 Handpicked Techniques to Perform Twitter Sentiment Analysis
Ever wondered whether it's possible to measure your audience's feelings on Twitter by using artificial intelligence tools and techniques? More importantly, is the analysis accurate and useful?
Also known as opinion mining, sentiment analysis is the process of text mining and using advanced Natural Language Processing (NLP) techniques to analyze the 'sentiment' or feelings of the content and understand whether it's positive, negative, or neutral.
Today's customers are voluntarily vocal on social media platforms and wish to engage with brands, government agencies, and the community at large on a host of topics, ranging from anything and everything. Hence, analyzing the Twitter dataset is no longer a nice-to-have; it has emerged as a necessity.
There are various ways to perform Twitter sentiment analysis. However, R and Python are the most widely-used techniques for Twitter data analysis.
Four Handpicked Techniques To Perform Twitter Sentiment Analysis
