sentimentanalys

Sentiment analysis, also called opinion mining, is a subfield of natural language processing and text analytics that seeks to identify and extract subjective information from text. The core aim is to determine the sentiment expressed, often in terms of polarity (positive, negative, neutral) and, in more advanced cases, emotions or attitudes toward specific aspects.

Methods used in sentiment analysis fall into two broad families. Lexicon-based approaches rely on sentiment lexicons—lists of words with associated sentiment scores—and apply rules to handle negation, intensification, and sarcasm. Machine learning approaches use labeled data to train classifiers that predict sentiment; recent progress emphasizes deep learning, which leverages neural networks and contextual embeddings to capture meaning across long text spans.

Common tasks include coarse-grained sentiment classification, fine-grained sentiment scoring, and aspect-based sentiment analysis, which assigns sentiment to particular features of an entity (for example, a product’s battery life vs. screen). Other tasks cover emotion detection, subjectivity classification, and sarcasm or irony detection.

Data and evaluation typically involve labeled corpora such as product reviews or social media posts. Performance is measured with metrics like accuracy, precision, recall, F1, and area under the ROC curve, often with cross-domain or cross-language testing to assess generalization.

Applications span marketing analytics, brand monitoring, customer feedback, and public opinion research. Challenges include sarcasm and negation handling, domain dependence, multilinguality, data noise, and biases in training data.