In this tutorial, we will explore text classification using Spacy word embeddings. We will be working with a news dataset, where the news articles are classified as either fake or real. Our goal is to build a machine learning model that can accurately classify news articles based on their content.
Contents
Loading the Dataset
Let’s start by loading the dataset into a pandas DataFrame. The dataset contains a CSV file with 9900 records in total, with columns for the news text and their corresponding labels (fake or real).
Preprocessing the Data
Before we can train our model, we need to preprocess the data. First, we will convert the labels into numerical values. We’ll assign the value 0 to fake news and 1 to real news. This will make it easier for the machine learning model to understand the data.
Next, we will convert the text column into word vectors using Spacy’s word embeddings. We’ll create a new column in the pandas DataFrame called “vector” to store the word vectors for each news article.
Training the Model
To train our model, we will split the dataset into a training set and a test set. We’ll use the “train_test_split” function from the sklearn library to accomplish this. The training set will consist of 80% of the data, while the test set will contain the remaining 20%.
Now that we have our training and test sets ready, we can import a classifier from sklearn and fit it to our training data. In this tutorial, we’ll use the multinomial naive Bayes model, which is commonly used for NLP tasks.
Evaluating the Model
Once our model is trained, we can evaluate its performance using the test set. We’ll use the “predict” method to make predictions on the test set and compare them to the actual labels. We’ll then calculate metrics such as precision, recall, and F1 score to assess the performance of our model.
Conclusion
In this tutorial, we explored the process of text classification using Spacy word vectors. We trained two different models – multinomial naive Bayes and K-nearest neighbors (KNN) – on a news dataset and evaluated their performance. The KNN model outperformed the multinomial naive Bayes model, achieving an accuracy of 99%. This demonstrates that KNN works well with dense representations of text, such as word vectors.
If you’re interested in trying out this tutorial, you can find the code and exercises here.
FAQs
Q: What is text classification?
A: Text classification is the process of automatically categorizing text documents into predefined classes or categories based on their content.
Q: What are word vectors?
A: Word vectors, also known as word embeddings, are numerical representations of words in a high-dimensional vector space. They capture semantic and syntactic meanings of words and are commonly used in natural language processing tasks.
Q: What is the multinomial naive Bayes model?
A: The multinomial naive Bayes model is a probabilistic classifier commonly used in text classification tasks. It assumes that the features (word counts) are conditionally independent given the class and follows a multinomial distribution.
Q: How do K-nearest neighbors (KNN) work?
A: K-nearest neighbors is a non-parametric classification algorithm that assigns a test sample to the majority class among its K nearest neighbors in the feature space.
Q: Where can I find the code and exercises for this tutorial?
A: You can find the code and exercises on the Techal website here.
