Sentiment analysis is one of the application areas of natural language processing. It is widely in use across industries and domains, and there is a big need for it in the industry. Every organization is aiming to focus customers and their needs. Hence, to understand voice and sentiment, the customer turns out to be the prime goal, as knowing the pulse of the customers leads to revenue generation. Nowadays, customers voice their sentiments through Twitter, Facebook, or blogs. It takes some work to refine that textual data and make it consumable. Let's look at how to do it in Python.
Here, verbatims of cinegoers have been taken from IMDB. This is shared on GitHub, too.
We will launch the libraries , as follows:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(color_codes=True)
import os
print(os.listdir...
