Sentiment analysis
(also opinion mining)
Sentiment analysis definition
Sentiment analysis is the computational process of identifying a writer’s or speaker’s attitude toward a particular topic, product, or service as positive, negative, or neutral. Social media platforms like Twitter and Facebook have often been the focus of sentiment analysis studies, especially during major events like elections or product launches. Companies use it to gauge public reception of products or advertisements. News agencies and governments utilize it to sense the public’s mood about policies or current events. It’s also vital in monitoring brand reputation and for crisis management.
How sentiment analysis works
Sentiment analysis typically involves natural language processing (NLP) and machine learning algorithms. A text is processed and broken down into components like tokens, phrases, or sentences. These components are then analyzed using pre-trained models or custom algorithms to determine their sentiment score.
History of sentiment analysis
The origins of sentiment analysis trace back to the late 20th century, evolving with the growth of online forums and product reviews in the early 2000s. As businesses recognized the importance of consumer feedback, tools to automate the categorization and analysis of massive volumes of text data became essential.