Tundeanalüüsi

Tundeanalüüsi, often translated as sentiment analysis or opinion mining, is a subfield of natural language processing (NLP) that focuses on identifying and extracting subjective information from text. Its primary goal is to determine the emotional tone or attitude expressed within a piece of writing, categorizing it as positive, negative, or neutral. This process involves analyzing words, phrases, and even sentence structures to infer the writer's feelings or opinions.

The applications of tundeanalüüsi are diverse and span across various industries. In marketing, businesses use it to gauge customer feedback on products and services by analyzing reviews, social media posts, and survey responses. This helps them understand brand perception and identify areas for improvement. In political science, tundeanalüüsi can be employed to track public opinion on candidates, policies, or events. The financial sector also utilizes it to analyze news articles and social media to predict stock market movements or assess investor sentiment.

Techniques for tundeanalüüsi range from lexicon-based approaches, which rely on predefined dictionaries of words with associated sentiment scores, to machine learning models. Machine learning methods, such as Naive Bayes, Support Vector Machines, and deep learning architectures like Recurrent Neural Networks (RNNs) and Transformers, are trained on large datasets of labeled text to learn patterns and predict sentiment. Challenges in tundeanalüüsi include handling sarcasm, irony, negation, and context-dependent sentiment, which can significantly alter the perceived emotion of a text. Despite these challenges, tundeanalüüsi remains a valuable tool for extracting insights from the vast amount of unstructured text data available today.