sentimentanalyse

Sentimentanalyse, also known as sentiment analysis, is a subfield of natural language processing and text analytics that aims to identify and extract subjective information from text. It focuses on determining the attitude or emotional tone of a speaker or writer, typically in relation to a product, service, topic, or entity.

Tasks in sentimentanalyse include polarity classification (positive, negative, neutral), aspect-based sentiment analysis (sentiment toward specific features), emotion detection, and stance or argumentation detection. More advanced work addresses sarcasm and negation handling, domain adaptation, and multilingual sentiment.

Approaches combine lexicon-based methods, which map words to sentiment scores, with data-driven techniques that learn from labeled data. Traditional supervised models and modern deep learning approaches, including transformer-based architectures, are widely used. Datasets commonly employed for evaluation include IMDb movie reviews, the Stanford Sentiment Treebank, Yelp reviews, and various SemEval tasks. Evaluation metrics typically include accuracy, precision, recall, F1, and area under the ROC curve for binary tasks, with multi-class or regression settings using adapted measures.

Applications of sentimentanalyse span market research, brand monitoring, customer service automation, and public opinion tracking. Key challenges include handling negation and sarcasm, domain-specific language, language variation and multilinguality, data imbalance, and annotation quality. Despite rapid advances, sentiment analysis remains difficult when context, culture, and irony influence interpretation, and results should be interpreted with these limitations in mind.