ARTICLE CLUSTERING USING WORD VECTOR

Click here to download the abstract project of understanding word vectors

click here to download the base paper of understanding word vectors

At datapro, we provide final year projects with source code in python for computer science students in Hyderabad, Visakhapatnam.

Abstract

The project “Article Clustering Using Word Vector” focuses on the development of a system that automatically groups articles into clusters based on their content similarity. By leveraging word vector representations, such as Word2Vec, GloVe, or BERT, the project aims to transform textual data into meaningful numerical vectors that capture the semantic relationships between words. These vectors are then used to measure the similarity between articles, enabling the system to cluster similar articles together. The goal is to enhance information retrieval, content organization, and topic discovery in large collections of text data, making it easier to manage and analyze vast amounts of information.

Existing System

Traditional article clustering methods often rely on simple keyword matching or bag-of-words approaches, which consider only the frequency of words without capturing their semantic meaning. These methods can result in clusters that are not truly representative of the underlying topics, as they fail to account for the context in which words are used. Additionally, existing systems may struggle with scalability, making it difficult to cluster large datasets efficiently. The lack of advanced natural language understanding in these methods limits their effectiveness in accurately grouping articles by topic.

Proposed System

The proposed system introduces an advanced approach to article clustering using word vectors to capture the semantic meaning of words and phrases in the text. By representing each article as a vector in a high-dimensional space, the system can measure the similarity between articles based on their content, rather than just the occurrence of specific words. The proposed system will utilize clustering algorithms, such as K-means, hierarchical clustering, or DBSCAN, to group articles with similar content together. This approach not only improves the accuracy of the clustering process but also enhances the system’s ability to handle large datasets efficiently.

Methodology

1. Data Collection:
   • Gather a large corpus of articles from various sources, ensuring diversity in topics, writing styles, and lengths.
   • Preprocess the data by removing stop words, punctuation, and other non-essential elements to clean the text.
2. Word Vector Representation:
   • Transform the textual data into word vectors using models like Word2Vec, GloVe, or BERT.
   • For each article, compute the average or weighted sum of the word vectors to obtain a single vector that represents the article as a whole.
3. Dimensionality Reduction (optional):
   • Apply techniques like PCA (Principal Component Analysis) or t-SNE (t-Distributed Stochastic Neighbor Embedding) to reduce the dimensionality of the word vectors, making them easier to visualize and cluster.
4. Clustering Algorithms:
   • Implement clustering algorithms such as K-means, hierarchical clustering, or DBSCAN to group similar articles together based on their vector representations.
   • Experiment with different clustering methods to determine which one provides the most meaningful clusters for the dataset.
5. Evaluation:
   • Evaluate the quality of the clusters using metrics such as Silhouette Score, Davies-Bouldin Index, or within-cluster sum of squares (WCSS).
   • Perform qualitative analysis by examining the content of the clusters to ensure they are coherent and relevant.
6. Visualization:
   • Visualize the clusters using tools like matplotlib, Seaborn, or Plotly to create 2D or 3D scatter plots that show the distribution of articles in the vector space.
   • Provide interactive visualizations that allow users to explore the clusters and understand the relationships between articles.
7. System Integration:
   • Develop an interface or API that allows users to input new articles and automatically assign them to existing clusters or create new ones.
   • Ensure that the system can handle dynamic datasets, where articles are continuously added, by updating the clusters as needed.

Technologies Used

• Programming Languages: Python for implementing the word vector models, clustering algorithms, and evaluation metrics.
• Natural Language Processing (NLP): Libraries such as NLTK, SpaCy, or Hugging Face Transformers for text preprocessing and word vector generation.
• Machine Learning Libraries: Scikit-learn for implementing clustering algorithms and dimensionality reduction techniques.
• Word Embedding Models: Word2Vec, GloVe, or BERT for generating word vectors that capture the semantic meaning of the text.
• Data Visualization Tools: Matplotlib, Seaborn, or Plotly for visualizing the clusters and word vectors.
• Database Management: SQL or NoSQL databases for storing the articles, word vectors, and clustering results.
• APIs and Integration: Flask or Django for creating APIs that allow interaction with the clustering system.

Expected Outcomes

By the end of this project, the following outcomes are expected:

• A functional system that can automatically cluster articles based on their content using word vector representations.
• Improved accuracy and relevance in clustering compared to traditional keyword-based methods.
• An interactive visualization tool that helps users explore and understand the relationships between different articles.
• A scalable solution that can handle large datasets and dynamically update clusters as new articles are added.

Applications

This project has various practical applications, including:

• Content Management Systems: Automatically organizing large collections of articles, blogs, or news into meaningful categories.
• Research and Academic Fields: Grouping related research papers or academic articles to facilitate literature reviews and topic exploration.
• Search Engines: Enhancing search results by clustering similar articles together, improving the relevance of search queries.
• News Aggregators: Automatically categorizing news articles by topic, making it easier for users to find related stories.