@inproceedings{khan-etal-2022-leveraging,
title = "Leveraging Semantic and Sentiment Knowledge for User-Generated Text Sentiment Classification",
author = "Khan, Jawad and
Ahmad, Niaz and
Alam, Aftab and
Lee, Youngmoon",
booktitle = "Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wnut-1.11",
pages = "101--105",
abstract = "Sentiment analysis is essential to process and understand unstructured user-generated content for better data analytics and decision-making. State-of-the-art techniques suffer from a high dimensional feature space because of noisy and irrelevant features from the noisy user-generated text. Our goal is to mitigate such problems using DNN-based text classification and popular word embeddings (Glove, fastText, and BERT) in conjunction with statistical filter feature selection (mRMR and PCA) to select relevant sentiment features and pick out unessential/irrelevant ones. We propose an effective way of integrating the traditional feature construction methods with the DNN-based methods to improve the performance of sentiment classification. We evaluate our model on three real-world benchmark datasets demonstrating that our proposed method improves the classification performance of several existing methods.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="khan-etal-2022-leveraging">
<titleInfo>
<title>Leveraging Semantic and Sentiment Knowledge for User-Generated Text Sentiment Classification</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jawad</namePart>
<namePart type="family">Khan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Niaz</namePart>
<namePart type="family">Ahmad</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aftab</namePart>
<namePart type="family">Alam</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Youngmoon</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Gyeongju, Republic of Korea</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Sentiment analysis is essential to process and understand unstructured user-generated content for better data analytics and decision-making. State-of-the-art techniques suffer from a high dimensional feature space because of noisy and irrelevant features from the noisy user-generated text. Our goal is to mitigate such problems using DNN-based text classification and popular word embeddings (Glove, fastText, and BERT) in conjunction with statistical filter feature selection (mRMR and PCA) to select relevant sentiment features and pick out unessential/irrelevant ones. We propose an effective way of integrating the traditional feature construction methods with the DNN-based methods to improve the performance of sentiment classification. We evaluate our model on three real-world benchmark datasets demonstrating that our proposed method improves the classification performance of several existing methods.</abstract>
<identifier type="citekey">khan-etal-2022-leveraging</identifier>
<location>
<url>https://aclanthology.org/2022.wnut-1.11</url>
</location>
<part>
<date>2022-10</date>
<extent unit="page">
<start>101</start>
<end>105</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Leveraging Semantic and Sentiment Knowledge for User-Generated Text Sentiment Classification
%A Khan, Jawad
%A Ahmad, Niaz
%A Alam, Aftab
%A Lee, Youngmoon
%S Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F khan-etal-2022-leveraging
%X Sentiment analysis is essential to process and understand unstructured user-generated content for better data analytics and decision-making. State-of-the-art techniques suffer from a high dimensional feature space because of noisy and irrelevant features from the noisy user-generated text. Our goal is to mitigate such problems using DNN-based text classification and popular word embeddings (Glove, fastText, and BERT) in conjunction with statistical filter feature selection (mRMR and PCA) to select relevant sentiment features and pick out unessential/irrelevant ones. We propose an effective way of integrating the traditional feature construction methods with the DNN-based methods to improve the performance of sentiment classification. We evaluate our model on three real-world benchmark datasets demonstrating that our proposed method improves the classification performance of several existing methods.
%U https://aclanthology.org/2022.wnut-1.11
%P 101-105
Markdown (Informal)
[Leveraging Semantic and Sentiment Knowledge for User-Generated Text Sentiment Classification](https://aclanthology.org/2022.wnut-1.11) (Khan et al., WNUT 2022)
ACL