Corporate Sentiment Analysis Tool

Problem Addressed

In enterprise operations, brand reputation, media signals, and emerging risks are scattered across web pages, news items, and comments. Manual review often fails in three ways: it focuses on isolated events instead of a time window, it relies on subjective sentiment judgment, and it can surface negative risks too late. @user_ccd82275/corporate-sentiment turns this workflow into data retrieval, sentiment analysis, trend summarization, and risk identification, which is useful for engineers and operations analysts who need weekly reports, monthly summaries, or early crisis warnings.

How It Works

The skill follows a fixed pipeline:
- Fetch data: run scripts/fetch_sentiment.py with --company-name and --time-range, supporting week and month, and receive a JSON list of sentiment records.
- Analyze data: use the model to classify positive, negative, and neutral ratios, extract key topics, events, spread trends, and sensitive content.
- Generate reports: produce a structured report with an overview, major events, sentiment breakdown, trend guidance, and risk warnings.

A week range is better for recent hotspots and sudden events, while a month range is better for longer-term patterns. If there is a high concentration of negative sentiment, the skill raises the risk level and suggests attention to public-relations response.

Boundaries

By default, the script may return mock data, which is intended for demos, tests, or workflow validation. To use a real sentiment API, follow references/sentiment-format.md to adjust the configuration, and make sure the company name and time range are complete and accurate. For sensitive or crisis-related sentiment, the skill can only assist with identification and summarization; it does not replace real-time human verification or emergency decision-making.