Member-only story
In today’s data-driven world, organizations are constantly looking for ways to extract valuable insights from vast amounts of textual data. Sentiment analysis, a branch of natural language processing (NLP), has emerged as a powerful tool for understanding the emotional tone and sentiment behind written text.
However, building and deploying sentiment analysis models can be a complex task, especially for those new to the field.
In this article, we’ll guide you through the process of developing a web-based sentiment analysis tool using Flask, a lightweight and popular Python web framework.
By the end of this tutorial, you’ll have a functional web application that can analyze the sentiment of text input, making it easier to gauge public opinion, monitor brand reputation, and gain valuable insights from customer feedback.
Setting up the Environment
Before we dive into the code, let’s ensure we have the necessary dependencies installed. We’ll be using Python, Flask, and the NLTK (Natural Language Toolkit) library for sentiment analysis.
