In data labeling, sentiment analysis is used to manually label text data with information about the emotion that it conveys. That is, it’s a task in which the goal is to classify the sentiment (e.g., positive, negative, neutral, happiness, anger, etc.) expressed in a piece of text. It is one of the various natural language processing (NLP) data labeling tasks.
We use sentiment analysis can be 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 assesses sentiments for products, services, or brands, identifying trends or patterns in customer sentiment over time.
Interpretation can be a tricky task in the daily routine, frequently people disagree by reading the same piece of information. Essentially, in the data labeling context it’s no different. 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.
