Social sentiment analysis is the use of natural language processing (NLP) and machine learning techniques to identify and extract subjective information from social media and other online sources. The goal of sentiment analysis is to determine the attitude, opinions, and emotions of the author or speaker towards a particular topic or product.
Sentiment analysis can be applied to various forms of digital content, including text, images, and videos, and can be used to extract valuable insights from large amounts of data. The AI-based sentiment analysis models are trained on large datasets of labeled text data and are capable of identifying patterns and features that indicate the sentiment of the text.
Sentiment analysis can be used in a variety of applications such as:
- Market research and customer feedback analysis
- Brand monitoring and reputation management
- Public opinion monitoring and political campaign analysis
- Product and service reviews analysis
It can also be used in business to understand customer engagement and feedback in real-time, which can be used to improve products and services, customer support, and overall customer experience.