This workflow automates the monitoring, analysis, and logging of Twitter content related to specific topics. It periodically fetches the latest tweets using the Apify API, performs sentiment analysis via GPT-4 to determine if tweets are positive, neutral, or negative, and then logs the results in a Google Sheet. Negative tweets trigger real-time alerts via Slack for further action. This setup is valuable for social media managers, brand monitoring, or PR teams aiming to stay responsive to online sentiment and automatically track trending topics.
### Workflow Steps:
1. **Schedule Trigger:** Runs every 6 hours to initiate data collection.
2. **Request Tweets from Apify API:** Sends a POST request to fetch recent tweets about a specified topic.
3. **Get Tweet Data:** Retrieves the latest tweets from the Apify dataset.
4. **Loop Over Tweets:** Iterates through each fetched tweet.
5. **Set Loop Fields:** Stores tweet details such as ID, URL, and text.
6. **Sentiment Analysis:** Uses OpenAI’s GPT-4 to analyze tweet sentiment and generate a concise reply.
7. **Check for Duplicates:** Ensures tweets are not processed multiple times.
8. **Sentiment Categorization:** Classifies sentiment as Positive, Neutral, or Negative.
9. **Conditional Actions:** Sends Slack alert for negative tweets, or logs all results into a Google Sheet, including the generated reply.
### Practical Use Cases:
– Monitoring brand reputation on Twitter.
– Automating responses to positive or negative customer feedback.
– Tracking trending hashtags and analyzing audience sentiment.
– Maintaining a record of social media engagement for analytics.
– Quickly identifying and acting on negative mentions to manage PR crises.
This workflow streamlines social listening efforts by integrating trending data fetching, sentiment classification, real-time alerts, and structured record-keeping.
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