sentiment-analyzer
Analyze text sentiment (positive/negative/neutral) with confidence scores, emotion detection, and visualization. Supports single text, CSV batch, and trend analysis.
survey-analyzer
Analyze survey responses with Likert scale analysis, cross-tabulations, sentiment scoring, and frequency distributions with visualizations.
nlp-processing
Text processing, sentiment analysis, LLMs, and NLP frameworks. Use for text classification, named entity recognition, or language models.
customer-review-aggregator
Aggregate and analyze customer reviews from G2, Capterra, Trustpilot, App Store, and other platforms. Performs sentiment analysis, identifies pain points, extracts feature feedback, generates marketing claims, and compares competitor reviews. Use when users need review analysis, competitive intelligence, or customer feedback insights.
meeting-intelligence-system
Analyze meeting transcripts to extract decisions, action items, blockers, sentiment, and generate follow-up emails. Use when user provides meeting notes, transcripts, or recordings and needs structured summaries or action tracking.
reddit-thread-analyzer
Analyze Reddit threads for sentiment, consensus opinions, top arguments, and discussion patterns. Use this when users want to understand Reddit community opinions, analyze discussions, or gather insights from subreddit conversations.
Sentiment Analysis
Classify text sentiment using NLP techniques, lexicon-based analysis, and machine learning for opinion mining, brand monitoring, and customer feedback analysis
Natural Language Processing
Build NLP applications using transformers library, BERT, GPT, text classification, named entity recognition, and sentiment analysis