How to build a sentiment analysis model in Python

Sentiment analysis is a natural language processing technique used to detect or extract the tone or feeling from a body of text, usually classifying it as positive, negative, or neutral. It can also be used to classify emotions, intentions, and predict user ratings. In this tutorial, we learn how to use Huggingface's pre-trained sentiment analysis models for analyzing customer reviews. We first load the dataset containing cell phone reviews from Kaggle and perform some data cleaning and preprocessing. Then, we pass our list of reviews to a BERT model finetuned for sentiment analysis tasks to obtain predicted ratings. Finally, we calculate an accuracy score to measure the model's performance in predicting actual review ratings and overall sentiment. The tutorial also demonstrates how to create a report showcasing the percentage of each sentiment category for all reviews for each company and a word cloud visualization to understand common words associated with different sentiments.