Detect emotions in text sentences
Predict NCM codes from product descriptions
Ask questions about air quality data with pre-built prompts or your own queries
eRAG-Election: AI กกต. สนับสนุนความรู้การเลือกตั้ง ฯลฯ
Find the best matching text for a query
Test SEO effectiveness of your content
Open LLM(CohereForAI/c4ai-command-r7b-12-2024) and RAG
Generate Shark Tank India Analysis
Humanize AI-generated text to sound like it was written by a human
Generate keywords from text
Classify Turkish text into predefined categories
fake news detection using distilbert trained on liar dataset
Rerank documents based on a query
Emotion Detection is a text analysis tool designed to identify and classify emotions within text sentences. It leverages advanced natural language processing (NLP) to recognize emotional tones such as happiness, sadness, anger, surprise, or neutral sentiments. This technology is particularly useful for analyzing social media posts, customer feedback, or personal communication to gain insights into emotional states.
• Emotion Identification: Accurately detects and classifies emotions in text. • Multi-Language Support: Works with various languages for global applicability. • Real-Time Analysis: Provides instant results for on-the-spot insights. • Customizable Models: Allows users to fine-tune detection for specific contexts. • Integration APIs: Easy-to-use APIs for seamless integration into applications. • User-Friendly Interface: Intuitive interface for non-technical users to interpret results.
What languages does Emotion Detection support?
Emotion Detection supports multiple languages, including English, Spanish, French, German, and many more, making it a versatile tool for global applications.
Can Emotion Detection handle slang or informal language?
Yes, the tool is designed to handle slang and informal language, ensuring accurate emotion detection in modern communication styles.
Is Emotion Detection suitable for real-time applications?
Absolutely! Emotion Detection is optimized for real-time analysis, making it ideal for live social media monitoring or customer service chatbots.