sentiment-analysis

Sentiment analysis, also known as opinion mining, involves using computational methods to identify and extract subjective information from text data. It goes beyond simply understanding the words themselves; it aims to decipher the underlying emotions, opinions, and attitudes conveyed by the author. This is achieved through various techniques like lexicon-based approaches (using pre-defined lists of words with associated sentiment scores), machine learning models trained on labeled datasets (e.g., classifying text as positive, negative, or neutral), and deep learning methods leveraging neural networks to capture more nuanced emotional expressions. Real-world applications include analyzing customer reviews to gauge product satisfaction, monitoring social media for brand reputation management, assessing public opinion towards political candidates, and even detecting signs of mental health issues in online communication. The output is typically a sentiment score or classification (e.g., positive, negative, neutral) indicating the overall emotional tone of the text.