The term "sentiment detection" (also known as "sentiment analysis") refers to the automated identification, classification, and evaluation of emotions, opinions, or attitudes expressed in text using natural language processing (NLP) and machine learning. The objective is to extract meaningful insights from unstructured data such as customer reviews, social media posts, or support tickets. Sentiment can be categorized as positive, negative, or neutral, and may also be identified in finer detail (e.g., angry, enthusiastic, disappointed).
Text classification by sentiment: Automatic categorization of text into sentiment types such as positive, negative, or neutral.
Tone analysis: Detection of emotional nuances such as frustration, joy, sarcasm, or concern.
Aspect-based sentiment analysis: Evaluation of individual product or service aspects (e.g., quality, price, customer service) by sentiment.
Multilingual sentiment recognition: Support for analyzing sentiment in various languages.
Real-time sentiment monitoring: Continuous detection of sentiment trends in social media or chat systems.
Sentiment metrics & dashboards: Visualization of aggregated sentiment scores across time periods, channels, or audiences.
CRM/helpdesk integration: Automatic prioritization or escalation of negative customer feedback.
Custom dictionaries & models: Adaptation of sentiment detection to industry-specific language or terminology.
A company analyzes product reviews in an online shop to determine whether customers react positively or negatively to a new product.
A social media monitoring tool identifies increasing negative sentiment toward a brand in real-time and notifies the PR team.
A call center uses sentiment detection to automatically escalate emails with a negative tone.
A hotel automatically assesses sentiment in online reviews regarding rooms, service, and value for money.
A software vendor integrates sentiment detection into chatbots to better respond to emotional reactions from users.