CS5720 - Many-to-One RNN (Sentiment Analysis)

Many-to-One Architecture

Many-to-One RNN processes a sequence of inputs (many) and produces a single output (one). Perfect for tasks like sentiment analysis, document classification, and sequence summarization.

Key Characteristics:

• Input: Variable-length sequence (words, tokens, time steps)
• Processing: RNN builds up context over entire sequence
• Output: Single prediction based on full sequence context
• Use Cases: Classification, summarization, encoding

📊 Perfect For:

Tasks where the entire sequence context determines a single decision or classification.

Sentiment Analysis Pipeline

Tokenization

Split text into individual words or tokens

Word Embeddings

Convert words to dense vector representations

RNN Processing

Sequential processing with hidden state accumulation

Final Hidden State

Extract final hidden state containing full context

Classification

Dense layer + softmax for sentiment prediction

Interactive Sentiment Analyzer

Enter Text for Sentiment Analysis

Enter text and click "Analyze Sentiment" to see the RNN processing pipeline

Predicted Sentiment:

Positive

Confidence: 85%

"The weather is beautiful today and I'm feeling great!"

Expected: Positive

"This service was terrible and completely ruined my day."

Expected: Negative

"The meeting is scheduled for tomorrow at 3 PM."

Expected: Neutral

"The food was good but the service was disappointing."

Expected: Mixed

Many-to-One RNN (Sentiment Analysis)

Many-to-One Architecture

Sentiment Analysis Pipeline

Interactive Sentiment Analyzer

Enter Text for Sentiment Analysis

Modal Title