3. Developing a Text Classifier

Activity 3.01: Developing End-to-End Text Classifiers

Solution

The following steps will help you implement this activity:

1. Open a Jupyter Notebook.
2. Insert a new cell and add the following code to import the necessary packages:

   import pandas as pd
   import seaborn as sns
   import matplotlib.pyplot as plt
   %matplotlib inline
   from nltk import word_tokenize
   from nltk.corpus import stopwords
   from nltk.stem import WordNetLemmatizer
   from sklearn.feature_extraction.text import TfidfVectorizer
   from sklearn.model_selection import train_test_split
   import nltk
   nltk.download('stopwords')
   nltk.download('punkt')
   nltk.download('wordnet')
   import warnings
   import string
   import re
   warnings.filterwarnings('ignore')
   from sklearn.metrics import accuracy_score, roc_curve, \
   classification_report, confusion_matrix, \
   precision_recall_curve, auc

3. Read a data file. It has three columns: is_political, headline, and short_description...