Topic Detection Using Keyword Clustering

Topic Detection Using Keyword Clustering

Abstract:

Identifying Topics Keyword Clustering is a proposed system for finding key topics in a set of documents. This system extracts relevant topics from a document collection using topic modeling. Topic modeling uses statistical models to identify document themes. Topic-related documents should use certain words more often. Words like “dog” and “bone” are more common in dog-related documents, while “cat” and “meow” are more common in cat-related documents. However, “the” and “is” appear equally in both types of documents.

Our system mathematically captures the fact that documents cover multiple topics in different proportions and examines the topics within a specific set of documents. The system extracts frequent keywords from the document collection. A clustering algorithm groups these keywords and identifies the collection’s main topics. Our system optimizes by considering term co-occurrence.

Web crawlers and users use this system. It aids web users searching for information. The system extracts relevant keywords from the document set when a user searches for a topic. Clustering these keywords gives users topic-related information quickly. Web users can easily find topic-related information.

Introduction:

Information retrieval and analysis require document topic detection. With so much information online, efficient methods for finding and organizing relevant topics are essential. Topic Detection Keyword Clustering is one solution. Topic modeling and clustering algorithms help the system find and extract topics from documents.

Objectives:

This system seeks to identify key topics in a set of documents. The system uses topic modeling and clustering algorithms to extract keywords, cluster them by co-occurrence, and identify the most relevant document topics. Topic-specific results help web users find information quickly.

Project Details:

The Topic Detection Using Keyword Clustering system follows a systematic approach to achieve its objective. The key steps involved in the system are as follows:

1. Document Collection: A set of documents is gathered, which may cover various topics in different proportions.
2. Keyword Extraction: The system extracts keywords from the document collection, identifying terms that occur frequently.
3. Clustering Algorithm: A clustering algorithm is employed to group the extracted keywords based on their co-occurrence.
4. Topic Identification: By analyzing the clusters of keywords, the system identifies the prominent topics within the document collection.
5. Information Retrieval: When a web user searches for a specific topic, the system matches the user’s query with the extracted keywords and presents topic-related information to the user.

Conclusion:

The Topic Detection Using Keyword Clustering system efficiently finds key topics in documents. Topic modeling and clustering algorithms extract keywords, cluster them by co-occurrence, and identify relevant topics. This system helps web crawlers and users find relevant information quickly and accurately. Web users can quickly find what they need by matching queries with relevant keywords and providing topic-related information.

Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.