Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147936
Title: Building SenticNet 7
Authors: Perh, Zhi Hao
Keywords: Engineering::Computer science and engineering::Computing methodologies::Document and text processing
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Perh, Z. H. (2021). Building SenticNet 7. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/147936
Project: SCSE20-0297
Abstract: The evolution of Artificial Intelligence (AI) has brought many possibilities in using machines to solve real-world problems and to bring conveniences to our daily lives. One of the examples that use AI in our daily lives is smart assistants like Google Assistant or Siri. This evolution has also brought new possibilities in natural language processing (NLP). From the beginning of the symbolic approach to the statistical approach and currently the neural network approach. The neural network approach is implemented with the concept of how the human brain processes data and this is also described as the deep learning technique. The sentiment analysis is usually the combination of the NLP and machine or deep learning techniques. It is a text analysis technique that identifies and determines the sentiment in a text such as positive, negative, or neutral. In this project, we will examine and evaluate the different methods of sentiment analysis techniques such as the lexicon approach and deep learning approach. This project aims to implement an error checking program as well as a semi-automated tool for synonyms and antonyms to enhance the SenticNet knowledge base. The error checking program utilizes the keyword extractor, sentiment analysis and synonyms functions. This program is used to perform checks with the existing SenticNet knowledge base for any discrepancy. The semi-automated tool is used to generate the list of synonyms and antonyms for a given word.
URI: https://hdl.handle.net/10356/147936
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP_Final_Report_PerhZhiHao_U1821914B.pdf
  Restricted Access
1.08 MBAdobe PDFView/Open

Page view(s)

126
Updated on May 19, 2022

Download(s) 50

39
Updated on May 19, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.