Classification using BoW

In this subsection, we will perform sentiment analysis and text classification based on BoW. Here, a bag of words is generated using the scikit-learn library.  Let's see how we perform sentiment analysis using BoW features in the following steps:

1. Load the dataset:

The first step to build a machine learning model is to load the dataset. Let's first read the data using the pandas read_csv() function:

# Import libraries
import pandas as pd

# read the dataset
df=pd.read_csv('amazon_alexa.tsv', sep='\t')

# Show top 5-records
df.head()

This results in the following output:

In the preceding output dataframe, we have seen that the Alexa review dataset has five columns: rating, date, variation, verified_reviews, and feedback.

2. Explore the dataset.

Let's plot the feedback column count to see how many positive and negative reviews the dataset has:

# Import seaborn
import seaborn as sns
import matplotlib.pyplot as plt

# Count...