Beginner-friendly overview of Python tools available for classifying sentiment in social media text. I discuss my experiences using different tools and offer suggestions to get you started on your own Python sentiment analysis journey!
In ancient Rome, public discourse happened at the Forum at the heart of the city. People gathered to exchange ideas and debate topics of social relevance. Today that public discourse has moved online to the digital forums of sites like Reddit, the microblogging arena of Twitter and other social media outlets. Perhaps as a researcher you are curious what people’s opinions are about a specific topic, or perhaps as an analyst you wish to study the effect of your company’s recent marketing campaign. Monitoring social media with sentiment analysis is a good way to gauge public opinion. Luckily, with Python there are many options available, and I will discuss the methods and tools I have experimented with, along with my thoughts about the experience.
On my learning journey, I started with the simplest option, TextBlob, and worked my way up to using transformers for deep learning with Pytorch and Tensorflow. If you are a beginner to Python and sentiment analysis, don’t worry, the next section provides background. Otherwise, feel free to skip ahead to my diagram below for a visual overview of the Python natural language processing (NLP) playground.
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