Unsupervised Sentiment Classification of Twitter Data using Emoticons

Autor:	Savitha Hiremath, S H Manjula, Venugopal K R
Rok vydání:	2021
Předmět:	Grammar Computer science business.industry media_common.quotation_subject Sentiment analysis computer.software_genre ComputingMethodologies_PATTERNRECOGNITION Emoticon Social media Artificial intelligence InformationSystems_MISCELLANEOUS business computer Natural language processing media_common
Zdroj:	2021 International Conference on Emerging Smart Computing and Informatics (ESCI).
DOI:	10.1109/esci50559.2021.9397026
Popis:	Twitter is a powerful social media where people share their opinion on various topics. Sentiment Analysis on twitter data gives the classification of opinion on a topic as positive, negative or neutral. Twitter messages are written informally and tweets are short. Hence, the classification of tweets by only considering the text part of the message does not give accurate results. To improve the classification accuracy we use Emotion Tokens like Emoticons or Emojis. Emotion Tokens are independent of language, grammar or size of the tweet. Considering Emotion tokens while classifying tweets will improve the accuracy of classification. In this paper, we propose unsupervised Sentiment classification on Twitter Data using Emoticons to improve the performance of classification.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::69edd258bf42bf45ab0ed16498300a73 https://doi.org/10.1109/esci50559.2021.9397026 Zobrazit plný text záznamu