Tutorial TF-IDF vs Word2Vec For Text Classification [How To In Python With And Without CNN]

by | Feb 15, 2023 | Data Science, Machine Learning, Natural Language Processing

Word2Vec for text classification

Word2Vec is a popular algorithm used for natural language processing and text classification. It is a neural network-based approach that learns distributed representations (also called embeddings) of words from a large corpus of text. These embeddings capture the semantic and syntactic relationships between terms, which can be used to improve text classification accuracy.

One common approach to using Word2Vec for text classification is to train the Word2Vec model on a large text dataset. This can be done using a tool like Gensim or TensorFlow. Then, once the embeddings have been introduced, they can be used as features in a machine learning model for text classification.

For example, one approach might be to represent each document as a vector by taking the average of the Word2Vec embeddings of the words in the document. Then, a classifier like logistic regression, random forests, or support vector machines can take this vector as an input.

Word2Vec embeddings turn words into vectors for text classification

Word2Vec embeddings turn words into vectors

Alternatively, you can also fine-tune the Word2Vec embeddings on your specific task by training a neural network to classify the text. In this approach, the Word2Vec embeddings are used as the input to the neural network, which is then trained to predict the class labels.

Word2Vec can be a powerful tool for text classification, primarily when combined with other machine learning techniques. However, it is vital to remember that there may be better approaches than this, and different algorithms like BERT or transformers may outperform them in some cases.

TF-IDF vs Word2Vec

TF-IDF (term frequency-inverse document frequency) and Word2Vec are popular algorithms used in natural language processing, but they serve different purposes.

TF-IDF is a simple and widely used method for text representation. It assigns weights to words in a document based on their frequency in the document and inverse frequency in the corpus. The idea is that words that are frequent in a document but rare in the corpus are likely to be important for that document’s meaning. This approach is commonly used for information retrieval and text classification tasks.

Word2Vec, on the other hand, is a more complex algorithm that learns vector representations (embeddings) of words based on their context in a large corpus of text. These embeddings show how words are related semantically and grammatically. They can be used for various natural language processing tasks, such as finding word analogies and similar words and putting texts into groups.

So, when it comes to text classification specifically, which one is better? The answer is it depends on the task and the data.

TF-IDF is a good choice when the documents in the dataset are relatively short and the vocabulary size is small. It is also computationally efficient and can handle large datasets. However, TF-IDF needs to capture the meaning of words or their relationships, so it may not be adequate for some text classification tasks.

Word2Vec, on the other hand, is better suited for larger and more complex datasets, where words have multiple meanings and relationships between them are essential. It is particularly effective when the task involves identifying similarities or relationships between documents, such as clustering or document retrieval. However, Word2Vec requires a large amount of data and computational resources to train, and it may perform poorly when limited training data is available.

In summary, both TF-IDF and Word2Vec have their strengths and weaknesses, and the choice of algorithm depends on the specific task, the data, and available resources.

Unsupervised text classification Word2Vec

Unsupervised text classification using Word2Vec involves using the Word2Vec embeddings of words to group similar documents together without needing labelled data. This can be useful when labelled data is limited and expensive to obtain.

One approach to unsupervised text classification using Word2Vec is to train the Word2Vec model on a large corpus of text and then represent each document as a vector by taking the average of the Word2Vec embeddings of the words in the document. This results in a vector representation of the document that captures the meaning and context of the words in the document.

Once the documents have been represented as vectors, clustering algorithms such as K-means, hierarchical clustering, or density-based clustering can be used to group similar documents. The clusters can then be looked at to see if they are about different things.

Another approach is to use Word2Vec embeddings to identify similar documents using techniques such as cosine similarity or euclidean distance. In this approach, each document is represented as a vector using the Word2Vec embeddings, and then the similarity between pairs of documents is computed. Documents with high similarity scores are then grouped.

Unsupervised text classification using Word2Vec can be a powerful tool for discovering latent themes and patterns in large amounts of unstructured text data. However, it should be noted that the quality of the clustering or similarity results depends on the quality of the Word2Vec embeddings and the clustering algorithm used. So, to get the best results, it’s essential to try out different hyperparameters and evaluation metrics.

Word2Vec for text classification example

Here’s an example of using Word2Vec for text classification:

Suppose you have a dataset of movie reviews, where each review is labelled as either positive or negative. Your task is to build a classifier that can predict the sentiment of a new review as either positive or negative.

  1. Pre-processing the text data: You first need to pre-process the text data by removing stop words, converting all text to lowercase, and removing punctuation. You can use tools such as NLTK or spaCy for this.
  2. Training the Word2Vec model: Once the data has been pre-processed, you can train a Word2Vec model using a tool such as Gensim. The model figures out how to represent words as vectors based on their use in the movie reviews dataset.
  3. Vectorizing the movie reviews: After training the Word2Vec model, you can represent each movie review as a vector by taking the average of the Word2Vec embeddings of the words in the review. This makes a vector representation of the review that shows how the words fit together and what they mean.
  4. Splitting the data: Split the dataset into training and testing sets.
  5. Building a classifier: Train a machine learning model such as logistic regression, random forests, or support vector machines using the vector representations of the movie reviews as input features and the sentiment labels as the target variable.
  6. Evaluating the model: Once the model has been trained, use metrics like accuracy, precision, recall, and F1-score to judge how well it did on the testing set.

This is just one example of using Word2Vec for text classification. Depending on the specific task and dataset, you may need to experiment with different hyperparameters and model architectures to achieve the best performance.

Text classification using Word2Vec Python

Here’s an example of how to use Word2Vec for text classification in Python using the scikit-learn library and Gensim Word2Vec model:

1. Install the required packages

You will need scikit-learn, Gensim, and NLTK packages. You can install them using pip as follows:

pip install scikit-learn gensim nltk

2. Load the data

Load the text data into Python, and split it into training and testing sets.

import pandas as pd
from sklearn.model_selection import train_test_split

data = pd.read_csv('movie_reviews.csv')
X_train, X_test, y_train, y_test = train_test_split(data['review'], data['sentiment'], test_size=0.2, random_state=42)

3. Preprocess the text data

Preprocess the text data by removing stop words, converting all text to lowercase, and removing punctuation using NLTK package.

from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import string

stop_words = set(stopwords.words('english'))
def preprocess(text):
    text = text.lower()
    text = ''.join([word for word in text if word not in string.punctuation])
    tokens = word_tokenize(text)
    tokens = [word for word in tokens if word not in stop_words]
    return ' '.join(tokens)

X_train = X_train.apply(preprocess)
X_test = X_test.apply(preprocess)

4. Train the Word2Vec model

Train a Word2Vec model on the preprocessed training data using Gensim package.

from gensim.models import Word2Vec

sentences = [sentence.split() for sentence in X_train]
w2v_model = Word2Vec(sentences, size=100, window=5, min_count=5, workers=4)

5. Vectorize the text data

Convert the preprocessed text data to a vector representation using the Word2Vec model.

import numpy as np

def vectorize(sentence):
    words = sentence.split()
    words_vecs = [w2v_model.wv[word] for word in words if word in w2v_model.wv]
    if len(words_vecs) == 0:
        return np.zeros(100)
    words_vecs = np.array(words_vecs)
    return words_vecs.mean(axis=0)

X_train = np.array([vectorize(sentence) for sentence in X_train])
X_test = np.array([vectorize(sentence) for sentence in X_test])

6. Train a classification model

Train a classification model such as logistic regression, random forests, or support vector machines using the vectorised training data and the sentiment labels.

from sklearn.linear_model import LogisticRegression

clf = LogisticRegression()
clf.fit(X_train, y_train)

7. Evaluate the model

Evaluate the performance of the classification model on the testing set with the accuracy, precision, recall and F1 score.

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

y_pred = clf.predict(X_test)
print('Accuracy:', accuracy_score(y_test, y_pred))
print('Precision:', precision_score(y_test, y_pred, pos_label='positive'))
print('Recall:', recall_score(y_test, y_pred, pos_label='positive'))
print('F1 score:', f1_score(y_test, y_pred, pos_label='positive'))

Here’s the complete code:

import pandas as pd
from sklearn.model_selection import train_test_split
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import string
from gensim.models import Word2Vec
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# Load the data
data = pd.read_csv('movie_reviews.csv')
X_train, X_test, y_train, y_test = train_test_split(data['review'], data['sentiment'], test_size=0.2, random_state=42)

# Preprocess the text data
stop_words = set(stopwords.words('english'))
def preprocess(text):
    text = text.lower()
    text = ''.join([word for word in text if word not in string.punctuation])
    tokens = word_tokenize(text)
    tokens = [word for word in tokens if word not in stop_words]
    return ' '.join(tokens)

X_train = X_train.apply(preprocess)
X_test = X_test.apply(preprocess)

# Train the Word2Vec model
sentences = [sentence.split() for sentence in X_train]
w2v_model = Word2Vec(sentences, size=100, window=5, min_count=5, workers=4)

# Vectorize the text data
def vectorize(sentence):
    words = sentence.split()
    words_vecs = [w2v_model.wv[word] for word in words if word in w2v_model.wv]
    if len(words_vecs) == 0:
        return np.zeros(100)
    words_vecs = np.array(words_vecs)
    return words_vecs.mean(axis=0)

X_train = np.array([vectorize(sentence) for sentence in X_train])
X_test = np.array([vectorize(sentence) for sentence in X_test])

# Train a classification model
clf = LogisticRegression()
clf.fit(X_train, y_train)

# Evaluate the model
y_pred = clf.predict(X_test)
print('Accuracy:', accuracy_score(y_test, y_pred))
print('Precision:', precision_score(y_test, y_pred, pos_label='positive'))
print('Recall:', recall_score(y_test, y_pred, pos_label='positive'))
print('F1 score:', f1_score(y_test, y_pred, pos_label='positive'))

Note that the above code assumes that the movie reviews data is stored in a CSV file with columns ‘review’ and ‘sentiment’, where the ‘review’ column contains the text of the movie reviews and the ‘sentiment’ column contains the sentiment labels (either ‘positive’ or ‘negative’). You will need to modify the code accordingly if your data is in a different format.

Word2Vec CNN text classification

In addition to using a logistic regression classifier with the vectorized word embeddings produced by Word2Vec, you can also use a convolutional neural network (CNN) for text classification. Here is an example of how to do this in Python:

import pandas as pd
import numpy as np
import string
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from gensim.models import Word2Vec
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Embedding, Conv1D, MaxPooling1D, Flatten, Dense
from tensorflow.keras.models import Sequential
from sklearn.model_selection import train_test_split

# Load the data
data = pd.read_csv('movie_reviews.csv')
X_train, X_test, y_train, y_test = train_test_split(data['review'], data['sentiment'], test_size=0.2, random_state=42)

# Preprocess the text data
stop_words = set(stopwords.words('english'))
def preprocess(text):
    text = text.lower()
    text = ''.join([word for word in text if word not in string.punctuation])
    tokens = word_tokenize(text)
    tokens = [word for word in tokens if word not in stop_words]
    return ' '.join(tokens)

X_train = X_train.apply(preprocess)
X_test = X_test.apply(preprocess)

# Tokenize the text data
tokenizer = Tokenizer()
tokenizer.fit_on_texts(X_train)

X_train = tokenizer.texts_to_sequences(X_train)
X_test = tokenizer.texts_to_sequences(X_test)

vocab_size = len(tokenizer.word_index) + 1

# Pad the sequences to a fixed length
max_length = 100
X_train = pad_sequences(X_train, maxlen=max_length, padding='post')
X_test = pad_sequences(X_test, maxlen=max_length, padding='post')

# Train the Word2Vec model
sentences = [sentence.split() for sentence in X_train]
w2v_model = Word2Vec(sentences, size=100, window=5, min_count=5, workers=4)

# Create a weight matrix for the embedding layer
embedding_matrix = np.zeros((vocab_size, 100))
for word, i in tokenizer.word_index.items():
    if word in w2v_model.wv:
        embedding_matrix[i] = w2v_model.wv[word]

# Define the CNN model
model = Sequential()
model.add(Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=max_length, trainable=False))
model.add(Conv1D(128, 5, activation='relu'))
model.add(MaxPooling1D(5))
model.add(Conv1D(128, 5, activation='relu'))
model.add(MaxPooling1D(5))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))

This code pre-processes and tokenises the text data using the same process. We then create a weight matrix for the embedding layer by extracting the Word2Vec embeddings for each word in the vocabulary of the text data.

We then define a CNN model that consists of an embedding layer, two convolutional layers with max pooling, a flattened layer, and two dense layers (one for hidden units and one for output units). Finally, we compile and train the model using the binary cross-entropy loss function and the Adam optimizer.

This model is much more complex than the logistic regression model and may take longer to train, but it has the potential to capture more complex patterns in the text data. The convolutional layers use filters to identify local patterns of words in the input sequences, and the max pooling layers reduce the dimensionality of the feature maps. The flattened layer converts the output of the convolutional layers to a 1D vector, which is then passed to the dense layers for classification.

In this example, we set the trainable parameter of the embedding layer to False to use the pre-trained Word2Vec embeddings. You can set this parameter to True if you have extensive data and want to learn the embeddings from scratch. However, this may require more training data and longer training times.

Overall, using a CNN with Word2Vec embeddings can be a powerful approach for text classification tasks, especially when dealing with more complex patterns in text data.

Conclusion

In conclusion, Word2Vec is a powerful tool for generating vector representations of words in text data. It can be used for text classification tasks by training a classifier on vectorized word embeddings. Word2Vec is especially useful for figuring out how words in text data relate to each other semantically. This can help text classification models work better.

Various approaches exist for using Word2Vec in text classification, including logistic regression and convolutional neural networks. Logistic regression is a simple and effective approach for getting started with Word2Vec-based text classification, while CNNs offer a more robust method for capturing complex patterns in text data. Both ways require pre-processing the text data, training the Word2Vec model, and training the classification model on vectorized word embeddings.

Word2Vec-based text classification is a valuable method for a wide range of natural language processing tasks, such as analyzing feelings, modelling topics, and classifying documents.

About the Author

Neri Van Otten

Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

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1 Comment

  1. SULAIMAN KHAN

    I solved my problem in NLP. Thanks

    Reply

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