Sentiment analysis is the process of understanding public opinion at scale, using publicly available social data text such as reviews, like posts on Yelp, Google, Amazon or even TripAdvisor, social media (public tweets, Instagram photos, or public Facebook posts and comments), or other written data like customer service chat logs.
Sentiment analysis is a useful tool for organizations that want to know how their consumers feel about their brand, products, campaigns, or competitors. Brands can track different emotions that arise in the conversations related to their brand, which include: anger, sadness, joy, fear, and disgust. Machine learning algorithms produce easy to read negative, neutral and positive judgments that allow for a better understanding of consumer opinion.
Sentiment Analysis allows companies to track opinions of an audience. From this text, the researchers at Scio Motus sort data into categories that show the topics that are central to the conversation being analyzed. Sentiment analysis can be used for market research, product analysis and research, and customer service. When understanding consumer opinions through social media companies are able to better understand an audience and how they feel.
Companies that keep the audience at the core of their mission will gain from insights about how the audience thinks and feels. These insights enable a company to reach out to a larger audience, and also alter messaging, campaigns, and even products to better suit their audience. Opinion mining customer opinions can allow marketers to understand how consumer feel during different stages of buying, this can help to boost a company’s bottom line and build relationships with customers.