Hugging Face Blog
· 2 min read
Getting Started with Sentiment Analysis on Twitter
Sentiment analysis is the automatic process of classifying text data according to their polarity, such as positive, negative and neutral. Companies leverage sentiment analysis of tweets to get a sense of how customers are talking about their products and services, get insights to drive business decisions, and identify product issues and potential PR crises early on.
In this guide, we will cover everything you need to learn to get started with sentiment analysis on Twitter. We'll share a step-by-step process to do sentiment analysis, for both, coders and non-coders. If you are a coder, you'll learn how to use the Inference API, a plug & play machine learning API for doing sentiment analysis of tweets at scale in just a few lines of code. If you don't know how to code, don't worry! We'll also cover how to do sentiment analysis with Zapier, a no-code tool that will enable you to gather tweets, analyze them with the Inference API, and finally send the results to Google Sheets ⚡️
Read along or jump to the section that sparks 🌟 your interest:
- What is sentiment analysis?
- How to do Twitter sentiment analysis with code?
- How to do Twitter sentiment analysis without coding?
Buckle up and enjoy the ride! 🤗
What is Sentiment Analysis?
Sentiment analysis uses machine learning to automatically identify how people are talking about a given topic. The most common use of sentiment analysis is detecting the polarity of text data, that is, automatically identifying if a tweet, product review or support ticket is talking positively, negatively, or neutral about something.
As an example, let's check out some tweets mentioning @Salesforce and see how they would be tagged by a sentiment analysis model:
Why do sentiment analysis on Twitter? Companies use this for a wide variety of use cases, but the two of the most common use cases are analyzing user feedback and monitoring mentions to detect potential issues early on.
Analyze Feedback on Twitter
Monitor Twitter Mentions to Detect Issues
Results:
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