In data labeling, sentiment analysis is used to manually label text data with information about the emotion that it conveys (e.g., positive, negative, neutral, happiness, anger, etc.). It is one of the various natural language processing (NLP) data labeling tasks.
Sentiment analysis is used to classify text data as positive, negative, or neutral, or to identify more fine-grained sentiments, such as happiness, anger, or sadness. It enables assessing the overall sentiment of a document or identifying specific sentiment expressions or phrases within a text.
Sentiment analysis is often used in machine learning and artificial intelligence projects to analyze social media data, customer feedback, or other forms of text data. The goal is to understand the opinions, attitudes, and emotions of the people who wrote the text. It is also used to assess sentiments for products, services, or brands, identifying trends or patterns in customer sentiment over time.Sentiment Analysis is subject to interpretation. This can make sentiment analysis difficult, as people often disagree when reading the same information. In the context of data labeling, this is still the case. Therefore, it is of great importance having the labelers aligned and trained to understand the nuances in written communication. Having a robust annotation guide also enables a good performance.
