Sentiment-Analyse, also known as opinion mining, is a subfield of Natürliche Sprachverarbeitung (NLP) that focuses on extracting and analyzing subjective information from text data. It aims to determine the emotional tone behind a body of text, categorizing it as positive, negative, or neutral. This process is crucial for various applications, including customer feedback analysis, brand monitoring, and social media sentiment tracking.
Die Methodik umfasst typischerweise mehrere Schritte:
- Datenerhebung: Gathering text data from various sources such as social media, reviews, blogs, and forums.
- Vorverarbeitung: Cleaning the data by removing noise, such as special characters, stop words, and irrelevant information. This step may also involve tokenization, stemming, and lemmatization.
- Merkmalsextraktion: Converting the text into a format suitable for analysis, often using techniques like bag-of-words, term frequency-inverse document frequency (TF-IDF), or word embeddings.
- Sentimentklassifikation: Applying machine learning algorithms or deep learning models (such as Rekurrente Neuronale Netze or transformers) to classify the sentiment of the text. Common algorithms include Support Vector Machines, Naive Bayes, and more advanced neural networks.
- Bewertung: Assessing the accuracy of the model using metrics such as precision, recall, and F1 score. This helps refine the analysis and improve the model’s performance.
Sentiment-Analyse wird in verschiedenen Branchen weit verbreitet eingesetzt, darunter Marketing, Finanzen und Gesundheitswesen, um Einblicke in das Verbraucherverhalten, Markttrends und die öffentliche Meinung zu gewinnen.