- Sentiment analysis is the process of classifying whether a block of text is positive, negative, or, neutral. The goal which Sentiment analysis tries to gain is to be analyzed people’s opinions in a way that can help businesses expand. It focuses not only on polarity (positive, negative & neutral) but also on emotions (happy, sad, angry, etc.).
- It uses various Natural Language Processing algorithms, such as Rule-based, Automatic, and Hybrid.
- Positive sentiment: Positive sentiment refers to the expression of favorable or optimistic emotions in a post. It indicates that the author has a positive opinion or attitude towards a particular topic or experience. In the context of LinkedIn, positive sentiment in posts may include sharing achievements, success stories, motivational content, or expressing gratitude for opportunities or experiences.
- Negative sentiment: Negative sentiment represents the expression of unfavorable or pessimistic emotions in a post. It indicates that the author has a negative opinion or attitude towards a particular topic or experience. In the context of LinkedIn, negative sentiment in posts may include sharing frustrations, challenges, disappointments, or expressing criticism towards certain aspects of work, industry, or experiences.
- Neutral sentiment: Neutral sentiment indicates the absence of strong positive or negative emotions in a post. It implies that the author remains unbiased or objective and presents information without expressing a clear opinion or attitude. In the context of LinkedIn, neutral sentiment in posts may include sharing informative articles, industry updates, news, or seeking objective feedback or opinions without displaying personal emotions.