Sentiment Analysis
Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) task that involves analyzing and determining the sentiment or opinion expressed in a piece of text, such as a customer review, social media post, or survey response. The goal of sentiment analysis is to understand and classify the subjective information conveyed in the text as positive, negative, or neutral.
In a sentiment analysis project, you typically work with a dataset containing text data and corresponding sentiment labels (e.g., positive, negative, neutral). The project involves several steps, including data cleaning and preprocessing, feature extraction (such as converting text to numerical representations), model training, and evaluation.
We are going to do this with the data we have from user comments on the Amazon site and building a machine learning model. This dataset contains 20,000 comments from the Amazon site about various products and programs. Each of these data has a label. positive means a positive opinion or negative means a negative opinion. (ignoring neutral opinions)