Text Augmentation For Emotion Classification In Microblog Text Using Similarity Scoring Based On Neural Embedding Models

Emotion classification can benefit from a larger pool of training data but manually expanding the emotion corpus is labour-intensive and time-consuming. Distant supervision can be used to collect large amount of training data in a short period of time using emotion word hashtags, but the collecte...

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Main Author:	Yong, Kuan Shyang
Format:	Thesis
Language:	English
Published:	2022
Subjects:	QA76.6 Electronic digital computers > Programming
Online Access:	http://eprints.usm.my/59117/1/YONG%20KUAN%20SHYANG%20-%20TESIS.pdf
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id	my-usm-ep.59117
record_format	uketd_dc
spelling	my-usm-ep.591172023-08-14T06:38:11Z Text Augmentation For Emotion Classification In Microblog Text Using Similarity Scoring Based On Neural Embedding Models 2022-08 Yong, Kuan Shyang QA76.6 Electronic digital computers -- Programming Emotion classification can benefit from a larger pool of training data but manually expanding the emotion corpus is labour-intensive and time-consuming. Distant supervision can be used to collect large amount of training data in a short period of time using emotion word hashtags, but the collected data may contain excessive noise. In this research, we proposed a text augmentation strategy to efficiently expand the size of positive examples for six emotion categories (happiness, anger, excitement, desperation, boredom and indifference) in EmoTweet-28 by exploiting tweets collected from distant supervision (DS) that are similar to the seed examples in EmoTweet-28 (ET-seed). Similarity scoring approach was used to compute to cosine similarity scores between each DS tweet and all ET-seed tweets under the same emotion category. Seven vector representations (USE, InferSent GloVe, InferSent fastText, Word2Vec, fastText, GloVe, and Bag-of-Words) were experimented to represent the tweets in the similarity scoring approach. DS tweets with high similarity scores were selected to become the augmented instances and annotated with emotion labels. The selection of DS tweets was divided into two categories which are threshold-based selection and fixed increment selection. In addition, we also modified the proposed text augmentation strategy by altering the seed sets used for similarity scoring using clustering and misclassified strategies. All augmented sets were evaluated by training a deep neural network classifier separately to distinguish between the presence or absence of specific emotion in tweets from the test set. 2022-08 Thesis http://eprints.usm.my/59117/ http://eprints.usm.my/59117/1/YONG%20KUAN%20SHYANG%20-%20TESIS.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA76.6 Electronic digital computers -- Programming
spellingShingle	QA76.6 Electronic digital computers -- Programming Yong, Kuan Shyang Text Augmentation For Emotion Classification In Microblog Text Using Similarity Scoring Based On Neural Embedding Models
description	Emotion classification can benefit from a larger pool of training data but manually expanding the emotion corpus is labour-intensive and time-consuming. Distant supervision can be used to collect large amount of training data in a short period of time using emotion word hashtags, but the collected data may contain excessive noise. In this research, we proposed a text augmentation strategy to efficiently expand the size of positive examples for six emotion categories (happiness, anger, excitement, desperation, boredom and indifference) in EmoTweet-28 by exploiting tweets collected from distant supervision (DS) that are similar to the seed examples in EmoTweet-28 (ET-seed). Similarity scoring approach was used to compute to cosine similarity scores between each DS tweet and all ET-seed tweets under the same emotion category. Seven vector representations (USE, InferSent GloVe, InferSent fastText, Word2Vec, fastText, GloVe, and Bag-of-Words) were experimented to represent the tweets in the similarity scoring approach. DS tweets with high similarity scores were selected to become the augmented instances and annotated with emotion labels. The selection of DS tweets was divided into two categories which are threshold-based selection and fixed increment selection. In addition, we also modified the proposed text augmentation strategy by altering the seed sets used for similarity scoring using clustering and misclassified strategies. All augmented sets were evaluated by training a deep neural network classifier separately to distinguish between the presence or absence of specific emotion in tweets from the test set.
format	Thesis
qualification_level	Master's degree
author	Yong, Kuan Shyang
author_facet	Yong, Kuan Shyang
author_sort	Yong, Kuan Shyang
title	Text Augmentation For Emotion Classification In Microblog Text Using Similarity Scoring Based On Neural Embedding Models
title_short	Text Augmentation For Emotion Classification In Microblog Text Using Similarity Scoring Based On Neural Embedding Models
title_full	Text Augmentation For Emotion Classification In Microblog Text Using Similarity Scoring Based On Neural Embedding Models
title_fullStr	Text Augmentation For Emotion Classification In Microblog Text Using Similarity Scoring Based On Neural Embedding Models
title_full_unstemmed	Text Augmentation For Emotion Classification In Microblog Text Using Similarity Scoring Based On Neural Embedding Models
title_sort	text augmentation for emotion classification in microblog text using similarity scoring based on neural embedding models
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer
publishDate	2022
url	http://eprints.usm.my/59117/1/YONG%20KUAN%20SHYANG%20-%20TESIS.pdf
_version_	1776101249607794688

Text Augmentation For Emotion Classification In Microblog Text Using Similarity Scoring Based On Neural Embedding Models

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