Sentiment analysis is the automatic process of classifying text data according to polarity—positive, negative, or neutral. Companies use it to gauge customer opinions, extract insights, and catch issues early. This guide covers both coding and no-code approaches.
What is Sentiment Analysis?
Sentiment analysis uses machine learning to identify how people talk about a topic. For example:
- "The more I use @salesforce the more I dislike it..." → Negative
- "That’s what I love about @salesforce..." → Positive
- "Coming Home: #Dreamforce Returns..." → Neutral
Previously, analyzing tweets was manual, slow, and error-prone. Now, AI models can do it in real-time at scale. Common use cases include analyzing feedback and monitoring mentions for potential crises.
Doing Sentiment Analysis with Code
With just a few lines of code, you can fetch tweets using Tweepy and analyze them via the Inference API. For instance, you can collect tweets mentioning @NotionHQ, run sentiment analysis, and visualize the results with charts.
Doing Sentiment Analysis Without Code
If you're not a coder, no worries! You can use Zapier to gather tweets, analyze them with the Inference API, and save the results to Google Sheets in a few steps:
- Get Tweets – Set up a Zapier trigger for new tweets matching a keyword.
- Analyze Sentiment – Connect the Hugging Face Inference API action.
- Save to Google Sheets – Add a Google Sheets action to store each tweet and its sentiment.
- Turn on Your Zap – Activate it to run automatically.
Wrap Up
Sentiment analysis on Twitter is now accessible to everyone, whether you code or not. Start analyzing tweets today to understand your audience better.