sentimentanalüüs

Sentimentanalüüs, also known as opinion mining, is a subfield of natural language processing (NLP) that aims to identify and extract subjective information from text. It involves determining the emotional tone behind a series of words, used to gain an understanding of the attitudes, opinions, and emotions expressed within an online mention. This can range from determining if a piece of text is positive, negative, or neutral, to identifying specific emotions like joy, anger, or sadness.

The process typically begins with preprocessing the text, which might include tokenization, stemming, and stop-word removal. Following this, various techniques are employed to analyze the sentiment. Lexicon-based approaches utilize dictionaries of words with pre-assigned sentiment scores. Machine learning models, on the other hand, are trained on large datasets of labeled text to learn patterns associated with different sentiments. These models can include algorithms like Naive Bayes, Support Vector Machines, and deep learning architectures.

Applications of sentiment analysis are widespread. Businesses use it to monitor brand perception, analyze customer reviews, and gauge market trends. In social sciences, it can be used to study public opinion on political issues or social events. It also finds use in areas like recommender systems and customer service to understand user satisfaction. The accuracy of sentiment analysis can be influenced by factors such as sarcasm, context, and domain-specific language.