Sentiment analysis is the process by which all of the content can be quantified to represent the ideas, beliefs, and opinions of entire sectors of the audience. The implications of sentiment analysis are hard to underestimate to increase the productivity of the business. Sentiment Analysis is one of those common NLP tasks that every Data Scientist need to perform.

For example, you are a student in an online course and you have a problem. You post it on the class forum. The sentiment analysis would be able to not only identify the topic you are struggling with, but also how frustrated or discouraged you are, and tailor their comments to that sentiment. This is already happening because the technology is already there.

Sentiment Analysis with Machine Learning

Hope you understood what sentiment analysis means. Now I’m going to introduce you to a very easy way to analyze sentiments with machine learning. The data I’ll be using includes 27,481 tagged tweets in the training set and 3,534 tweets in the test set. You can easily download the data from here. Now let’s start with this task by looking at the data using pandas:

import pandas as pd
training = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")
print("Training data: \n",training.head())
print("Test Data: \n",test.head())

#by aman kharwal #data analysis