Classifying a document into a pre-defined category is a common problem, for instance, classifying an email as spam or not spam. In this case there is an instance to be classified into one of two possible classes, i.e. binary classification.

However, there are other scenarios, for instance, when one needs to classify a document into one of more than two classes, i.e., multi-class, and even more complex when each document can be assigned to more than one class, i.e. multi-label or multi-output classification.

In this post, I will show an approach to classify a document into a set of pre-defined categories using different supervised classifiers and text representations. I will use the IMDB dataset of movies. Although the dataset contains several pieces of information about a movie, for the scope of this post I will only use the plot of the movie and the genre(s) on which the movie is classified.


In order to create the dataset for this experiment you need to download genres.list and plot.list files from a mirror FTP, and then parse files in order to associate the titles, plots, and genres.

I’ve already done this step, and parsed both files in order to generate a single file, available here movies_genres.csv, containing the plot and the genres associated to each movie.

Pre-processing and cleaning

I started by doing some exploratory analysis on the IMDB dataset

%matplotlib inline

import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv("movies_genres.csv", delimiter='\t')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 117352 entries, 0 to 117351
Data columns (total 30 columns):
title          117352 non-null object
plot           117352 non-null object
Action         117352 non-null int64
Adult          117352 non-null int64
Adventure      117352 non-null int64
Animation      117352 non-null int64
Biography      117352 non-null int64
Comedy         117352 non-null int64
Crime          117352 non-null int64
Documentary    117352 non-null int64
Drama          117352 non-null int64
Family         117352 non-null int64
Fantasy        117352 non-null int64
Game-Show      117352 non-null int64
History        117352 non-null int64
Horror         117352 non-null int64
Lifestyle      117352 non-null int64
Music          117352 non-null int64
Musical        117352 non-null int64
Mystery        117352 non-null int64
News           117352 non-null int64
Reality-TV     117352 non-null int64
Romance        117352 non-null int64
Sci-Fi         117352 non-null int64
Short          117352 non-null int64
Sport          117352 non-null int64
Talk-Show      117352 non-null int64
Thriller       117352 non-null int64
War            117352 non-null int64
Western        117352 non-null int64
dtypes: int64(28), object(2)
memory usage: 26.9+ MB

We have a total of 117 352 movies and each of them is associated with 28 possible genres. The genres columns simply contain a 1 or 0 depending on whether the movie is classified into that particular genre or not. This means the multi-label binary mask is already provided in this file.

Next, we are going to calculate the absolute number of movies per genre. Note: each movie can be associated with more than one genre, we just want to know which genres have more movies.

df_genres = df.drop(['plot', 'title'], axis=1)
counts = []
categories = list(df_genres.columns.values)
for i in categories:
    counts.append((i, df_genres[i].sum()))
df_stats = pd.DataFrame(counts, columns=['genre', '#movies'])
genre #movies
0 Action 12381
1 Adult 61
2 Adventure 10245
3 Animation 11375
4 Biography 1385
5 Comedy 33875
6 Crime 15133
7 Documentary 12020
8 Drama 46017
9 Family 15442
10 Fantasy 7103
11 Game-Show 2048
12 History 2662
13 Horror 2571
14 Lifestyle 0
15 Music 2841
16 Musical 596
17 Mystery 12030
18 News 3946
19 Reality-TV 12338
20 Romance 19242
21 Sci-Fi 8658
22 Short 578
23 Sport 1947
24 Talk-Show 5254
25 Thriller 8856
26 War 1407
27 Western 2761
df_stats.plot(x='genre', y='#movies', kind='bar', legend=False, grid=True, figsize=(15, 8))

Since the Lifestyle has 0 instances we can just remove it from the data set

df.drop('Lifestyle', axis=1, inplace=True)

One thing that notice when working with this dataset is that there are plots written in different languages. Let’s use langedetect tool to identify the language in which the plots are written

from langdetect import detect
df['plot_lang'] = df.apply(lambda row: detect(row['plot'].decode("utf8")), axis=1)
en    117196
nl       120
de        14
da         6
it         6
pt         2
fr         2
no         2
hu         1
es         1
sl         1
sv         1
Name: plot_lang, dtype: int64

There are other languages besides English, let’s just keep English plots, and save this to a new file.

df = df[df.plot_lang.isin(['en'])]
df.to_csv("movies_genres_en.csv", sep='\t', encoding='utf-8', index=False)

Vector Representation and Classification

For vector representation and I will use two Python packages:

To train supervised classifiers, we first need to transform the plot into a vector of numbers. I will explore 3 different vector representations:

  • TF-IDF weighted vectors
  • word2vec embeddings
  • doc2vec embeddings

After having these vector representations of the text we can train supervised classifiers to train unseen plots and predict the genres on which they fall.


Based on the bag-of-words model, i.e., no word order is kept. I considered TF-IDF weighted vectors, composed of different n-grams size, namely: uni-grams, bi-grams and tri-grams. I also experimentally eliminated words that appear in more than a given number of documents. All these features can be easily configured with TfidfVectorizer class.

The max_df parameter is used for removing terms that appear too frequently, i.e., max_df = 0.50 means “ignore terms that appear in more than 50% of the documents”. The ngram_range parameter selects how large is the sequence of words to be considered.

tfidf__max_df: (0,25 0.50, 0.75)
tfidf__ngram_range: ((1, 1), (1, 2), (1, 3))


Under this scenario, a movie plot is represented by a single real-value dense vector based on the word embeddings associated with each word. This is done by selecting words from the plot, based on their part-of-speech (PoS)-tags, and then summing their word embeddings and averaging them into a single vector. I used the GoogleNews-vectors, which have a dimension of 300 and are derived from English corpora. For this experiment I selected only adjectives and nouns;


Doc2Vec is an extension made over Word2Vec, which tries to model a single document or paragraph as a unique single real-value dense vector. You can read more about it in the original paper. I will use the gensim implementation to derive vectors based on a single document.

At the end of this post, you have a link to the complete code, showing how to generate embeddings with word2vec and doc2vec.

Load pre-processed data:

First, we are going to load the pre-processed and cleaned data into the proper data structures which serve as input for the sklearn classifiers:

data_df = pd.read_csv("movies_genres_en.csv", delimiter='\t')

# split the data, leave 1/3 out for testing
data_x = data_df[['plot']].as_matrix()
data_y = data_df.drop(['title', 'plot', 'plot_lang'], axis=1).as_matrix()
stratified_split = StratifiedShuffleSplit(n_splits=2, test_size=0.33)
for train_index, test_index in stratified_split.split(data_x, data_y):
    x_train, x_test = data_x[train_index], data_x[test_index]
    y_train, y_test = data_y[train_index], data_y[test_index]

# transform matrix of plots into lists to pass to a TfidfVectorizer
train_x = [x[0].strip() for x in x_train.tolist()]
test_x = [x[0].strip() for x in x_test.tolist()]

After loading the data I also split the data into two sets:

  • 2/3 ~ 66.6% of the data for tuning the parameters of the classifiers
  • 1/3 ~ 33.3% will be used to test the performance of the classifiers

To achieve this I used the StratifiedShuffleSplit class which returns stratified randomised folds, preserving the percentage of samples for each class.

In order to experiment with different features for the text representation and tune the different parameters of the classifiers I used sklearn Pipeline and GridSearchCV. I also use another class, to help transform binary classifiers into multi-label/multi-output classifiers, concretely OneVsRestClassifier, this class wraps ups the process of training a classifier for each possible class.

I considered the following supervise algorithms

  • Naive Bayes
  • SVM linear
  • Logistic Regression

Note that Naive Bayes and Logistic Regression inherently support multi-class, but we are in a multi-label scenario, that’s the reason why even these are wrapped in the OneVsRestClassifier process.

Naive Bayes

pipeline = Pipeline([
    ('tfidf', TfidfVectorizer(stop_words=stop_words)),
    ('clf', OneVsRestClassifier(MultinomialNB(
        fit_prior=True, class_prior=None))),
parameters = {
    'tfidf__max_df': (0.25, 0.5, 0.75),
    'tfidf__ngram_range': [(1, 1), (1, 2), (1, 3)],
    'clf__estimator__alpha': (1e-2, 1e-3)

SVM linear

pipeline = Pipeline([
    ('tfidf', TfidfVectorizer(stop_words=stop_words)),
    ('clf', OneVsRestClassifier(LinearSVC()),
parameters = {
    'tfidf__max_df': (0.25, 0.5, 0.75),
    'tfidf__ngram_range': [(1, 1), (1, 2), (1, 3)],
    "clf__estimator__C": [0.01, 0.1, 1],
    "clf__estimator__class_weight": ['balanced', None],

Logistic Regression

pipeline = Pipeline([
    ('tfidf', TfidfVectorizer(stop_words=stop_words)),
    ('clf', OneVsRestClassifier(LogisticRegression(solver='sag')),
parameters = {
    'tfidf__max_df': (0.25, 0.5, 0.75),
    'tfidf__ngram_range': [(1, 1), (1, 2), (1, 3)],
    "clf__estimator__C": [0.01, 0.1, 1],
    "clf__estimator__class_weight": ['balanced', None],

Parameter tunning through GridSearchCV

We then pass the built pipeline into a GridSearchCV object, and find the best parameters for both the bag-of-words representation and the classifier.

grid_search_tune = GridSearchCV(
    pipeline, parameters, cv=2, n_jobs=2, verbose=3), train_y)

print("Best parameters set:")
print grid_search_tune.best_estimator_.steps

# measuring performance on test set
print "Applying best classifier on test data:"
best_clf = grid_search_tune.best_estimator_
predictions = best_clf.predict(test_x)

print classification_report(test_y, predictions, target_names=genres)



Naive Bayes best parameters set:
    TfidfVectorizer: max_df=0.25, ngram_range=(1, 3)
    MultinomialNB: alpha=0.001

Linear SVM best parameters set:
    TfidfVectorizer: max_df=0.25 ngram_range=(1, 2)
    LinearSVC:  C=1, class_weight='balanced'

LogisticRegression best parameters set:
    TfidfVectorizer:  max_df=0.75, ngram_range=(1, 2)
    LogisticRegression: C=1, class_weight='balanced'

                     precision    recall   f1-score   

Naive Bayes            0.95        0.76      0.84     
Linear SVM             0.89        0.86      0.87     
LogisticRegression     0.70        0.89      0.78     


Linear SVM best parameters set:
    LinearSVC:  C=1, class_weight=None

LogisticRegression best parameters set:
    LogisticRegression: C=1, class_weight=None

                     precision    recall   f1-score   

Linear SVM             0.68        0.37      0.45
LogisticRegression     0.67        0.40      0.48


Linear SVM best parameters set:
    LinearSVC:  C=0.1, class_weight=None

LogisticRegression best parameters set:
    LogisticRegression: C=1, class_weight=None

                     precision    recall   f1-score   

Linear SVM             0.69        0.31      0.40
LogisticRegression     0.65        0.36      0.45


The best results are achieved with a Linear SVM and TF-IDF representation of the text, below you can see the results by genre.

Best parameters set:
    TfidfVectorizer(max_df=0.25, ngram_range=(1, 2))
        LinearSVC(C=1, class_weight='balanced')

              precision    recall  f1-score   support

     Action       0.89      0.84      0.86      4046
      Adult       1.00      0.67      0.80        21
  Adventure       0.89      0.81      0.85      3415
  Animation       0.92      0.86      0.89      3780
  Biography       0.95      0.58      0.72       491
     Comedy       0.89      0.87      0.88     11236
      Crime       0.86      0.90      0.88      4974
Documentary       0.84      0.83      0.84      3986
      Drama       0.89      0.94      0.91     15110
     Family       0.89      0.84      0.86      5160
    Fantasy       0.90      0.79      0.84      2381
  Game-Show       0.95      0.87      0.91       730
    History       0.86      0.70      0.77       853
     Horror       0.93      0.66      0.77       826
      Music       0.92      0.82      0.87       951
    Musical       0.96      0.58      0.73       190
    Mystery       0.82      0.85      0.84      3918
       News       0.91      0.83      0.87      1337
 Reality-TV       0.89      0.85      0.87      4057
    Romance       0.90      0.90      0.90      6472
     Sci-Fi       0.90      0.83      0.86      2853
      Short       1.00      0.48      0.65       183
      Sport       0.91      0.73      0.81       616
  Talk-Show       0.89      0.87      0.88      1775
   Thriller       0.86      0.78      0.82      2914
        War       0.91      0.79      0.84       447
    Western       0.96      0.86      0.91       874

avg / total       0.89      0.86      0.87     83596

The embedding method shows very low results, the representation based on the word2vec was just a naive way to get sentence embeddings, more robust methods could be explored like concatenating each word’s vector into a single vector and giving it as input to a neural network.

The doc2vec vectors were generated with gensim out-of-the-box, some parameter tuning on the vectors generation process might give better results.

Also, word2vec and doc2vec, since they have a much lower dimension, i.e. 300 compared to 50 000 up to 100 000 of the TF-IDF weighted vectors, could probably be achieved with a non-linear kernel.

The full code for this post is available on my GitHub: