Create a Pipeline to Perform Sentiment Analysis using NLP
This article was published as a part of the Data Science Blogathon.
Overview
- Every basic fundamental and building block which is required for Sentiment Analysis.
- I’ve used an easy approach to explain all the basic concepts so that even a beginner reader would be able to get a thorough understanding of all the concepts.
- Topics: Preprocessing text, Vocabulary Corpus, Feature Extraction (Sparse Representation and Frequency Dictionary), Logistic Regression model for sentiment analysis.
Sentiment Analysis is a supervised Machine Learning technique that is used to analyze and predict the polarity of sentiments within a text (either positive or negative).
It is often used by businesses and companies to understand their user’s experience, emotions, responses, etc. so that they can improve the quality and flexibility of their products and services.
Now, let’s dive deep into understanding how this sentiment analysis technique is used by machine learning engineers to examine sentiments of various texts.
Gathering Data
Data is the heart of every machine learning problem.