Meeleoluanalüüs

Meeleoluanalüüs, commonly translated as mood analysis or sentiment analysis, is a subfield of natural language processing (NLP) that aims to identify and extract subjective information from text. This subjective information can include opinions, emotions, attitudes, and feelings expressed by an author. The primary goal of meeleoluanalüüs is to determine the emotional tone behind a series of words, used to gain an understanding of the attitudes of a speaker, writer, or other subject.

The process typically involves analyzing text data to classify its sentiment as positive, negative, or neutral. More advanced techniques can also identify specific emotions such as joy, anger, sadness, or surprise. Meeleoluanalüüs is widely used in various applications, including customer feedback analysis, brand monitoring, market research, and social media analysis. By understanding public opinion and customer sentiment, businesses can make informed decisions, improve products and services, and manage their reputation effectively.

Several approaches are employed in meeleoluanalüüs. Lexicon-based methods rely on dictionaries of words annotated with their sentiment scores. Machine learning approaches, on the other hand, train models on large datasets of labeled text to learn patterns associated with different sentiments. These models can be rule-based or statistical, with deep learning models showing particularly promising results in recent years due to their ability to capture complex linguistic nuances. The accuracy of meeleoluanalüüs can be influenced by factors such as sarcasm, irony, and context-dependent language.