How This Workflow Works
This workflow analyzes laptop product reviews by using a large language model (LLM) to determine sentiment and, if required, identify sentiment aspects—highlighting the specific features, topics, or components the sentiment refers to. The results are then visualized to reveal sentiment by products and trends over time, enabling actionable and data-driven insights.
Key Features:
- Assigns sentiment to each review using advanced AI models
- Extracts specific aspects and reasoning behind each sentiment classification
- Visualizes sentiment by product and trends over time for easier interpretation and decision-making
Step-by-step:
1. Apply AI-Powered Sentiment Analysis:
Each review is sent to a large language model, which evaluates the text and assigns a sentiment label: positive, negative or neutral. Depending on user preferences, the model can also identify the specific aspect being discussed and provide a brief explanation for the assigned sentiment.
2. Aggregate and Summarize Sentiment Data:
The workflow groups the sentiment results to calculate the number of positive, neutral, or negative reviews for each product and for each time period. This aggregation helps reveal patterns and shifts in customer perception.
3. Visualize and Share Insights:
The summarized sentiment data is displayed using bar charts and line plots, making it easier to compare products and track sentiment changes over time. Users can review the original feedback alongside the AI-generated sentiment and supporting details.