@inproceedings{shanmugavadivel-etal-2024-innovationengineers,
title = "{I}nnovation{E}ngineers@{D}ravidian{L}ang{T}ech-{EACL} 2024: Sentimental Analysis of {Y}ou{T}ube Comments in {T}amil by using Machine Learning",
author = "Shanmugavadivel, Kogilavani and
Subramanian, Malliga and
V, Palanimurugan and
D, Pavul chinnappan",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Nadarajan, Rajeswari and
Ravikiran, Manikandan",
booktitle = "Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.dravidianlangtech-1.44",
pages = "262--265",
abstract = "There is opportunity for machine learning and natural language processing research because of the growing volume of textual data. Although there has been little research done on trend extraction from YouTube comments, sentiment analysis is an intriguing issue because of the poor consistency and quality of the material found there. The purpose of this work is to use machine learning techniques and algorithms to do sentiment analysis on YouTube comments pertaining to popular themes. The findings demonstrate that sentiment analysis is capable of giving a clear picture of how actual events affect public opinion. This study aims to make it easier for academics to find high-quality sentiment analysis research publications. Data normalisation methods are used to clean an annotated corpus of 1500 citation sentences for the study. .For classification, a system utilising one machine learning algorithm{---}K-Nearest Neighbour (KNN), Na ̈{\i}ve Bayes, SVC (Support Vector Machine), and RandomForest{---}is built. Metrics like the f1-score and correctness score are used to assess the correctness of the system.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="shanmugavadivel-etal-2024-innovationengineers">
<titleInfo>
<title>InnovationEngineers@DravidianLangTech-EACL 2024: Sentimental Analysis of YouTube Comments in Tamil by using Machine Learning</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kogilavani</namePart>
<namePart type="family">Shanmugavadivel</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Malliga</namePart>
<namePart type="family">Subramanian</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Palanimurugan</namePart>
<namePart type="family">V</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pavul</namePart>
<namePart type="given">chinnappan</namePart>
<namePart type="family">D</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-03</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages</title>
</titleInfo>
<name type="personal">
<namePart type="given">Bharathi</namePart>
<namePart type="given">Raja</namePart>
<namePart type="family">Chakravarthi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruba</namePart>
<namePart type="family">Priyadharshini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anand</namePart>
<namePart type="given">Kumar</namePart>
<namePart type="family">Madasamy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sajeetha</namePart>
<namePart type="family">Thavareesan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elizabeth</namePart>
<namePart type="family">Sherly</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rajeswari</namePart>
<namePart type="family">Nadarajan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Manikandan</namePart>
<namePart type="family">Ravikiran</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">St. Julian’s, Malta</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>There is opportunity for machine learning and natural language processing research because of the growing volume of textual data. Although there has been little research done on trend extraction from YouTube comments, sentiment analysis is an intriguing issue because of the poor consistency and quality of the material found there. The purpose of this work is to use machine learning techniques and algorithms to do sentiment analysis on YouTube comments pertaining to popular themes. The findings demonstrate that sentiment analysis is capable of giving a clear picture of how actual events affect public opinion. This study aims to make it easier for academics to find high-quality sentiment analysis research publications. Data normalisation methods are used to clean an annotated corpus of 1500 citation sentences for the study. .For classification, a system utilising one machine learning algorithm—K-Nearest Neighbour (KNN), Na ̈ıve Bayes, SVC (Support Vector Machine), and RandomForest—is built. Metrics like the f1-score and correctness score are used to assess the correctness of the system.</abstract>
<identifier type="citekey">shanmugavadivel-etal-2024-innovationengineers</identifier>
<location>
<url>https://aclanthology.org/2024.dravidianlangtech-1.44</url>
</location>
<part>
<date>2024-03</date>
<extent unit="page">
<start>262</start>
<end>265</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T InnovationEngineers@DravidianLangTech-EACL 2024: Sentimental Analysis of YouTube Comments in Tamil by using Machine Learning
%A Shanmugavadivel, Kogilavani
%A Subramanian, Malliga
%A V, Palanimurugan
%A D, Pavul chinnappan
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Nadarajan, Rajeswari
%Y Ravikiran, Manikandan
%S Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F shanmugavadivel-etal-2024-innovationengineers
%X There is opportunity for machine learning and natural language processing research because of the growing volume of textual data. Although there has been little research done on trend extraction from YouTube comments, sentiment analysis is an intriguing issue because of the poor consistency and quality of the material found there. The purpose of this work is to use machine learning techniques and algorithms to do sentiment analysis on YouTube comments pertaining to popular themes. The findings demonstrate that sentiment analysis is capable of giving a clear picture of how actual events affect public opinion. This study aims to make it easier for academics to find high-quality sentiment analysis research publications. Data normalisation methods are used to clean an annotated corpus of 1500 citation sentences for the study. .For classification, a system utilising one machine learning algorithm—K-Nearest Neighbour (KNN), Na ̈ıve Bayes, SVC (Support Vector Machine), and RandomForest—is built. Metrics like the f1-score and correctness score are used to assess the correctness of the system.
%U https://aclanthology.org/2024.dravidianlangtech-1.44
%P 262-265
Markdown (Informal)
[InnovationEngineers@DravidianLangTech-EACL 2024: Sentimental Analysis of YouTube Comments in Tamil by using Machine Learning](https://aclanthology.org/2024.dravidianlangtech-1.44) (Shanmugavadivel et al., DravidianLangTech-WS 2024)
ACL