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03-Text-Classification-Assessment-Solution

Updated
17 Sept 2026
2 min read
4 sections
Page 95 of 117
Python & Machine Learning

Text Classification Assessment - Solution

Goal: Given a set of text movie reviews that have been labeled negative or positive

For more information on this dataset visit http://ai.stanford.edu/~amaas/data/sentiment/

Complete the tasks in bold below!

Task: Perform imports and load the dataset into a pandas DataFrame For this exercise you can load the dataset from '../DATA/moviereviews.csv'{:python}.

python
# CODE HERE
python
df = pd.read_csv('../DATA/moviereviews.csv')
python
df.head()
Out · table
label
review
0
neg
how do films like mouse hunt get into theatres...
12 more lines
1
neg
some talented actresses are blessed with a dem...
2
pos
this has been an extraordinary year for austra...
3
pos
according to hollywood movies made in last few...
4
neg
my first press screening of 1998 and already i...

TASK: Check to see if there are any missing values in the dataframe.

python
#CODE HERE
python
# Check for NaN values:
df.isnull().sum()
Out · result
label      0
review    35
dtype: int64

TASK: Remove any reviews that are NaN

python
df = df.dropna()

TASK: Check to see if any reviews are blank strings and not just NaN. Note: This means a review text could just be: "" or " " or some other larger blank string. How would you check for this? Note: There are many ways! Once you've discovered the reviews that are blank strings, go ahead and remove them as well. Click me for a big hint

python
df['review'].str.isspace().sum()
Out · result
27
python
df[df['review'].str.isspace()]
Out · table
label
review
57
neg
71
51 more lines
pos
147
pos
151
pos
283
pos
307
pos
313
neg
323
pos
343
pos
351
neg
427
pos
501
neg
633
pos
675
neg
815
neg
851
neg
977
neg
1079
neg
1299
pos
1455
neg
1493
pos
1525
neg
1531
neg
1763
neg
1851
neg
1905
pos
1993
pos
python
df = df[~df['review'].str.isspace()]
python
df.info()
Out · stdout
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1938 entries, 0 to 1999
Data columns (total 2 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  -----
4 more lines
 0   label   1938 non-null   object
 1   review  1938 non-null   object
dtypes: object(2)
memory usage: 45.4+ KB

TASK: Confirm the value counts per label:

python
#CODE HERE
python
df['label'].value_counts()
Out · result
pos    969
neg    969
Name: label, dtype: int64

EDA on Bag of Words

Bonus Task: Can you figure out how to use a CountVectorizer model to get the top 20 words (that are not english stop words) per label type? Note, this is a bonus task as we did not show this in the lectures. But a quick cursory Google search should put you on the right path. Click me for a big hint

python
from sklearn.feature_extraction.text import CountVectorizer
python
cv = CountVectorizer(stop_words='english')
python
matrix = cv.fit_transform(df[df['label']=='neg']['review'])
freqs = zip(cv.get_feature_names(), matrix.sum(axis=0).tolist()[0])    
# sort from largest to smallest
print("Top 20 words used for Negative reviews.")
print(sorted(freqs, key=lambda x: -x[1])[:20])
Out · stdout
Top 20 words used for Negative reviews.
[('film', 4063), ('movie', 3131), ('like', 1808), ('just', 1480), ('time', 1127), ('good', 1117), ('bad', 997), ('character', 926), ('story', 908), ('plot', 888), ('characters', 838), ('make', 813), ('really', 743), ('way', 734), ('little', 696), ('don', 683), ('does', 666), ('doesn', 648), ('action', 635), ('scene', 634)]
python
matrix = cv.fit_transform(df[df['label']=='pos']['review'])
freqs = zip(cv.get_feature_names(), matrix.sum(axis=0).tolist()[0])    
# sort from largest to smallest
print("Top 20 words used for Positive reviews.")
print(sorted(freqs, key=lambda x: -x[1])[:20])
Out · stdout
Top 20 words used for Positive reviews.
[('film', 5002), ('movie', 2389), ('like', 1721), ('just', 1273), ('story', 1199), ('good', 1193), ('time', 1175), ('character', 1037), ('life', 1032), ('characters', 957), ('way', 864), ('films', 851), ('does', 828), ('best', 788), ('people', 769), ('make', 764), ('little', 751), ('really', 731), ('man', 728), ('new', 702)]

Training and Data

TASK: Split the data into features and a label (X and y) and then preform a train/test split. You may use whatever settings you like. To compare your results to the solution notebook, use test_size=0.20, random_state=101&#123;:python&#125;

python
#CODE HERE
python
from sklearn.model_selection import train_test_split

X = df['review']
y = df['label']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101)

Training a Mode

TASK: Create a PipeLine that will both create a TF-IDF Vector out of the raw text data and fit a supervised learning model of your choice. Then fit that pipeline on the training data.

python
#CODE HERE
python
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
from sklearn.naive_bayes import MultinomialNB
python
pipe = Pipeline([('tfidf', TfidfVectorizer()),('svc', LinearSVC()),])
python
# Feed the training data through the pipeline
pipe.fit(X_train, y_train)
Out · result
Pipeline(steps=[('tfidf', TfidfVectorizer()), ('svc', LinearSVC())])

TASK: Create a classification report and plot a confusion matrix based on the results of your PipeLine.

python
#CODE HERE
python
from sklearn.metrics import classification_report,plot_confusion_matrix
python
preds = pipe.predict(X_test)
python
print(classification_report(y_test,preds))
Out · stdout
              precision    recall  f1-score   support

         neg       0.81      0.86      0.83       191
         pos       0.85      0.81      0.83       197
3 more lines
    accuracy                           0.83       388
   macro avg       0.83      0.83      0.83       388
weighted avg       0.83      0.83      0.83       388
python
plot_confusion_matrix(pipe,X_test,y_test)
Out · result
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1f370d0b790>
Out · plot
Output 1

Great job!