Twitter Sentiment Analysis using Machine Learning Techniques: A Case Study of ChatGPT

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AU5X9KDEX" target="_blank" >RIV/00216208:11320/23:U5X9KDEX - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174706225&doi=10.1109%2fInC457730.2023.10262961&partnerID=40&md5=fb0c26950ae77605c354ecf080a9f81d" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174706225&doi=10.1109%2fInC457730.2023.10262961&partnerID=40&md5=fb0c26950ae77605c354ecf080a9f81d</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/InC457730.2023.10262961" target="_blank" >10.1109/InC457730.2023.10262961</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Twitter Sentiment Analysis using Machine Learning Techniques: A Case Study of ChatGPT

Popis výsledku v původním jazyce

"ChatGPT is a powerful AI bot developed by OpenAI. This technology has the potential to generate a humanlike response. ChatGPT is a pre-trained system capable of generating chat and understanding human speech. This paper identified the responses of ChatGPT users through related tweets with the help of natural language processing and machine learning techniques. This paper uses textBlob, VADER and human annotation to find the sentiment of each tweet; countvectorizer is used for feature extraction and different machine learning algorithms to classify them into different classes. LeXmo is used to identify the various sentiment analyses, and it is observed that positive and trust emotions are higher than other sentiments. SVM with 10-fold cross-validation shows better results than other techniques. © 2023 IEEE."

Název v anglickém jazyce

Twitter Sentiment Analysis using Machine Learning Techniques: A Case Study of ChatGPT

Popis výsledku anglicky

"ChatGPT is a powerful AI bot developed by OpenAI. This technology has the potential to generate a humanlike response. ChatGPT is a pre-trained system capable of generating chat and understanding human speech. This paper identified the responses of ChatGPT users through related tweets with the help of natural language processing and machine learning techniques. This paper uses textBlob, VADER and human annotation to find the sentiment of each tweet; countvectorizer is used for feature extraction and different machine learning algorithms to classify them into different classes. LeXmo is used to identify the various sentiment analyses, and it is observed that positive and trust emotions are higher than other sentiments. SVM with 10-fold cross-validation shows better results than other techniques. © 2023 IEEE."

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

"Proc. IEEE InC4 - IEEE Int. Conf. Contemp. Comput. Commun."

ISBN

979-835033577-4

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

—

Místo konání akce

Cham

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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