Sentiment analysis is the use of computer software to identify opinions expressed in text and classify them, for example as positive, negative, or neutral. These applications can provide very important data for businesses that need to monitor perceptions of their products or brands on social media and other online channels, as well as for organizations seeking to understand public opinion about prominent figures, issues, or current events.

Sentiment analysis is one of the most common applications of natural language processing (NLP), which is the use of artificial intelligence (AI) and related algorithmic approaches to allow computers to understand, interpret, and even communicate using human language. Sentiment analysis uses machine learning algorithms and deep learning approaches using artificial neural networks to conduct the machine translation and analysis of text, typically using TensorFlow or Python programming.

Sentiment analysis and other natural language processing (NLP) skills are a valuable asset for careers in data science, with a growing number of businesses and other organizations looking to take advantage of this technology to help understand their customers or the general public. From conducting analysis of brand perceptions on social media to creating chatbots that can understand and respond appropriately to users’ emotions, sentiment analysis can be harnessed for an expanding range of emerging applications.

An understanding of how to use the insights generated by sentiment analysis is also important for today’s digital marketing professionals, who need to understand how their brands and products are being discussed on social media networks like Twitter, Facebook, and Instagram and adjust their communications strategies appropriately. According to the Bureau of Labor Statistics, marketing managers earned a median annual salary of $135,900 in 2019, and “advertising managers who can navigate the digital world should have the best prospects.”

Yes! There are a wealth of opportunities to learn about all kinds of data science skills on Coursera, including courses and Specializations spanning multiple courses about natural language processing (NLP) and sentiment analysis. You can learn from top-ranked institutions like University of Washington, University of Michigan, and deeplearning.ai, or by completing step-by-step tutorials alongside experienced instructors with Coursera’s Guided Projects. Regardless of how you choose to learn, taking courses online lets you build valuable skills in sentiment analysis on a flexible schedule that fits into your existing studies or career.