Sentiment Analysis
Challenge
The company was inundated with textual feedback ranging from venue conditions, event organization, to overall experience. While the data was rich in insights, the unstructured nature made it cumbersome to derive actionable conclusions manually. Moreover, the company needed to respond to feedback promptly to maintain its reputation.
Solution
- LLM for Text Parsing: We employed a Large Language Model to parse the raw text of the feedback, breaking it down into analyzable components.
- Sentiment Analysis Engine: Building on the parsed data, we implemented a Sentiment Analysis engine that categorized the feedback into "Positive," "Neutral," or "Negative" sentiments.
- Real-time Dashboard: To facilitate quick responses, a real-time dashboard was set up to display sentiment analysis results, highlighting areas needing immediate attention.
Result
The introduction of the sentiment analysis engine significantly streamlined the client's feedback review process. It allowed the events company to instantly gauge public sentiment, enabling them to make real-time adjustments to their services and address issues promptly. This technological leap not only improved their customer satisfaction rates but also gave them a competitive edge in the fast-paced events industry.
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