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Sentiment Trend Analysis

Track customer sentiment trends over time using natural language processing and sentiment scoring

Complexity: Moderate | Duration: 15-30m | Category: Research/Intelligence

Tags: sentiment-analysis NLP trend-analysis customer-intelligence time-series

Workflow Diagram​

Steps​

Step 1: Data collection​

Agent: research

agent - Gather reviews, social mentions, feedback over time period

Step 2: Text preprocessing​

Agent: data

engineering - Clean, tokenize, normalize text data

Step 3: Sentiment scoring​

Agent: data

engineering - Apply sentiment analysis (VADER, TextBlob, or ML model)

Step 4: Topic extraction​

Agent: research

agent - Extract topics and themes using NLP (LDA, keywords)

Step 5: Time​

Agent: series aggregation

data-engineering - Aggregate sentiment scores by day/week/month

Step 6: Trend calculation​

Agent: data

engineering - Calculate moving averages, trend lines, change points

Step 7: Visualization​

Agent: frontend

developer - Create sentiment trend charts with topic breakdown

Step 8: Insight generation​

Agent: research

agent - Identify significant shifts and underlying causes

Usage​

To execute this workflow:

/workflow research/intelligence/sentiment-trend-analysis.workflow

See other workflows in this category for related automation patterns.