The document proposes a model to summarize text, extract important keywords, and create a knowledge graph from large amounts of text such as customer reviews. The goal is to analyze the text to determine both positive and negative sentiments about a product based on what customers say. The proposed model would preprocess the input text through steps like tokenization, stopword filtering, lemmatization and part-of-speech tagging. It would then summarize the text, classify the sentiment, and use the information to build a knowledge graph.

1. Text Summarization and
Building Knowledge Graph
Presented by
Hossain Md Delowar
Project Proposal on
Semantic Web

2. Goal
• We have lot of text
• Thousands of reviews about your product
• Summarizing the text, finding out important keywords and creating
knowledge graph
• So that one can figure out many things that were previously unknown
• For example: Any business organization may come across to know
The good things and
× The bad things related to a particular product
• Based on customers review

3. Example

4. Proposed Model
Input Text
(Review comments)
Classify
(Based on Sentiment)
Preprocessing
Tokenize
Filter Stop words
Lemmatization
POS Tagging
Text Summarize
Knowledge Graph
Creation
Sentiment
Model

5. Thank you