In this tutorial, we will explore news classification using Gensim word vectors. News classification involves categorizing news articles as either fake or real. To do this, we will utilize a CSV file containing the news data.
Contents
Gensim Word Vectors and News Classification
Gensim Word Embeddings Overview
Gensim word embeddings offer a powerful way to analyze words and their similarities. In our previous video, we discussed the concept of word embeddings and explored Gensim functionality. We also learned how to find the similarity between words and obtain word vectors.
Loading the Dataset
To begin, we will load the dataset into a pandas dataframe, a popular Python library for data manipulation and analysis. We can accomplish this using the pd.read_csv() function.
Data Pre-Processing
Before we proceed with training our model, we need to perform some pre-processing steps. This involves removing stop words and obtaining the lemmatized versions of the words. We will use the Spacy library for this purpose.
Vectorization of Text
To transform our text data into a numeric representation, we employ a technique known as vectorization. Specifically, we will use the Gensim word embedding method to convert the text into word embeddings. We will also calculate the mean vector to represent the entire sentence.
Training and Testing
Once we have transformed our text data into its numeric representation, we can proceed with training our model. In this tutorial, we will utilize a gradient boosting classifier, a commonly used machine learning algorithm. After training, we can evaluate the performance of our model using precision, recall, and F1 score.
Prediction and Evaluation
To validate our model’s performance, we will make predictions on new news articles. We can assess whether our model correctly classified them as real or fake. Additionally, we will generate a confusion matrix to visualize the results.
Conclusion
In this tutorial, we explored news classification using Gensim word vectors. We learned how to preprocess the data, convert the text into vectorized representations, and train a model for news classification. The accuracy of the model was impressive, achieving a precision and recall score of 98%.
To access the complete tutorial and code, please visit the Techal website.
FAQs
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Q: What are Gensim word vectors?
A: Gensim word vectors are a powerful tool for analyzing words and their similarities. They offer a way to convert words into numeric representations. -
Q: What is the purpose of news classification?
A: News classification involves categorizing news articles as either fake or real. This helps in detecting misinformation and promoting reliable sources of information. -
Q: What is the performance of the trained model?
A: The trained model achieved an accuracy of 98% with high precision, recall, and F1 scores for both real and fake news classifications.
