Time expression and named entity recognition for sentiment analysis

Sentiment analysis has become an important area of natural language processing (NLP). Today, when it comes to sentiment analysis tasks, sub-symbolic artificial intelligence (AI) approaches such as neural networks and deep learning are much more popular and widely used compared to classic symbolic AI...

Full description

Saved in:
Bibliographic Details
Main Author: Tan, Jordan Rei Yao
Other Authors: Erik Cambria
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/157140
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-157140
record_format dspace
spelling sg-ntu-dr.10356-1571402022-05-09T03:16:18Z Time expression and named entity recognition for sentiment analysis Tan, Jordan Rei Yao Erik Cambria School of Computer Science and Engineering cambria@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Document and text processing Sentiment analysis has become an important area of natural language processing (NLP). Today, when it comes to sentiment analysis tasks, sub-symbolic artificial intelligence (AI) approaches such as neural networks and deep learning are much more popular and widely used compared to classic symbolic AI approaches. Despite this, there have also been attempts to bridge the gap between the two approaches and integrate them. The Sentic Computing framework is a novel approach that leverages on the strengths of both symbolic and sub-symbolic AI for sentiment analysis. It identifies the core concepts in text using linguistic patterns, generalizes them through deep learning, and identifies the polarities associated with them in a knowledge base to determine the overall text sentiment. This ensemble application taps on the strengths of both top-down and bottom-up learning, and gives machines some logical reasoning ability for the natural language. However, it does not rely on any prior training for polarity prediction on a text. On the other hand, sub-symbolic approaches learn the polarity of text based on identifying patterns in training data. Hence, they are able to take the particular context or lingo of a text type into account when predicting its polarity. Understanding this difference, one aim of this research is to explore how Sentic Computing—an ensemble application of symbolic and sub-symbolic AI—compares with popular sub-symbolic approaches when it comes to sentiment analysis. In addition, it is noted that most research on sentiment analysis is focused on analysis at the sentence-level. However, most of the time, we wish to understand what is the entity mentioned in a text that a positive, neutral, or negative sentiment is for. This task gets more complicated when a text contains multiple named entities, with different sentiments for each of them. This highlights the importance of named entity recognition for sentiment analysis. Therefore, in this research, we also propose a novel method to perform entity-level sentiment analysis. The method looks at the grammatical structure of sentences to extract named entities as well as their corresponding descriptions, in order to identify the sentiments for each of them. Experimental results show that our proposed method for entity-level sentiment analysis yields more insights compared to traditional sentence-level sentiment analysis. Bachelor of Engineering (Computer Science) 2022-05-09T03:16:18Z 2022-05-09T03:16:18Z 2022 Final Year Project (FYP) Tan, J. R. Y. (2022). Time expression and named entity recognition for sentiment analysis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157140 https://hdl.handle.net/10356/157140 en SCSE21-0233 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Document and text processing
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Document and text processing
Tan, Jordan Rei Yao
Time expression and named entity recognition for sentiment analysis
description Sentiment analysis has become an important area of natural language processing (NLP). Today, when it comes to sentiment analysis tasks, sub-symbolic artificial intelligence (AI) approaches such as neural networks and deep learning are much more popular and widely used compared to classic symbolic AI approaches. Despite this, there have also been attempts to bridge the gap between the two approaches and integrate them. The Sentic Computing framework is a novel approach that leverages on the strengths of both symbolic and sub-symbolic AI for sentiment analysis. It identifies the core concepts in text using linguistic patterns, generalizes them through deep learning, and identifies the polarities associated with them in a knowledge base to determine the overall text sentiment. This ensemble application taps on the strengths of both top-down and bottom-up learning, and gives machines some logical reasoning ability for the natural language. However, it does not rely on any prior training for polarity prediction on a text. On the other hand, sub-symbolic approaches learn the polarity of text based on identifying patterns in training data. Hence, they are able to take the particular context or lingo of a text type into account when predicting its polarity. Understanding this difference, one aim of this research is to explore how Sentic Computing—an ensemble application of symbolic and sub-symbolic AI—compares with popular sub-symbolic approaches when it comes to sentiment analysis. In addition, it is noted that most research on sentiment analysis is focused on analysis at the sentence-level. However, most of the time, we wish to understand what is the entity mentioned in a text that a positive, neutral, or negative sentiment is for. This task gets more complicated when a text contains multiple named entities, with different sentiments for each of them. This highlights the importance of named entity recognition for sentiment analysis. Therefore, in this research, we also propose a novel method to perform entity-level sentiment analysis. The method looks at the grammatical structure of sentences to extract named entities as well as their corresponding descriptions, in order to identify the sentiments for each of them. Experimental results show that our proposed method for entity-level sentiment analysis yields more insights compared to traditional sentence-level sentiment analysis.
author2 Erik Cambria
author_facet Erik Cambria
Tan, Jordan Rei Yao
format Final Year Project
author Tan, Jordan Rei Yao
author_sort Tan, Jordan Rei Yao
title Time expression and named entity recognition for sentiment analysis
title_short Time expression and named entity recognition for sentiment analysis
title_full Time expression and named entity recognition for sentiment analysis
title_fullStr Time expression and named entity recognition for sentiment analysis
title_full_unstemmed Time expression and named entity recognition for sentiment analysis
title_sort time expression and named entity recognition for sentiment analysis
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/157140
_version_ 1734310177468841984