sentimentanalüüsis

Sentiment analysis, also known as opinion mining, is a subfield of natural language processing (NLP) that seeks to determine the emotional tone behind a body of text. It can identify subjective information and gauge attitudes, opinions, emotions, and intentions expressed in written or spoken language. The primary goal is to classify text into categories such as positive, negative, or neutral.

The process typically involves breaking down text into smaller units, like sentences or words, and then analyzing these units for sentiment-bearing terms. Lexicon-based approaches rely on predefined dictionaries of words with associated sentiment scores. Machine learning approaches, on the other hand, train models on large datasets of labeled text to learn patterns that indicate sentiment. These models can capture more nuanced expressions and context.

Sentiment analysis has a wide range of applications. Businesses use it to monitor brand reputation, understand customer feedback from reviews and social media, and gauge public reaction to products or marketing campaigns. In political science, it can be used to analyze public opinion on candidates or policies. It also finds use in areas like financial market prediction and content recommendation systems. The effectiveness of sentiment analysis can vary depending on the complexity of the language, the presence of sarcasm, irony, or domain-specific jargon.