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Sentiment analysis of greek tweets and hashtags using a sentiment lexicon

Published: 01 October 2015 Publication History

Abstract

The rapid growth of social media has rendered opinion and sentiment mining an important area of research with a wide range of applications. We focus on the Greek language and the microblogging platform "Twitter", investigating methods for extracting sentiment of individual tweets as well population sentiment for different subjects (hashtags). The proposed methods are based on a sentiment lexicon. We compare several approaches for measuring the intensity of "Anger", "Disgust", "Fear", "Happiness", "Sadness", and "Surprise". To evaluate the effectiveness of our methods, we develop a benchmark dataset of tweets, manually rated by two humans. Our automated sentiment results seem promising and correlate to real user sentiment. Finally, we examine the variation of sentiment intensity over time for selected hashtags, and associate it with real-world events.

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    cover image ACM Other conferences
    PCI '15: Proceedings of the 19th Panhellenic Conference on Informatics
    October 2015
    438 pages
    ISBN:9781450335515
    DOI:10.1145/2801948
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 01 October 2015

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    Author Tags

    1. sentiment lexicon
    2. sentiment mining
    3. social media
    4. twitter

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    PCI '15 Paper Acceptance Rate 64 of 148 submissions, 43%;
    Overall Acceptance Rate 190 of 390 submissions, 49%

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    • (2024)A Comparative Sentiment Analysis of Greek Clinical Conversations Using BERT, RoBERTa, GPT-2, and XLNetBioengineering10.3390/bioengineering1106052111:6(521)Online publication date: 21-May-2024
    • (2024)Analyzing the worldwide perception of the Russia-Ukraine conflict through TwitterJournal of Big Data10.1186/s40537-024-00921-w11:1Online publication date: 22-May-2024
    • (2023)PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource LanguagesApplied Sciences10.3390/app1305326513:5(3265)Online publication date: 3-Mar-2023
    • (2023)Sentiment Analysis in Greek Clinical Conversations: A Comparative Study of BERT, VADER, and Lexicon Approaches2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM58861.2023.10385833(4800-4806)Online publication date: 5-Dec-2023
    • (2022)Investigating the COVID-19 vaccine discussions on Twitter through a multilayer network-based approachInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10309559:6Online publication date: 1-Nov-2022
    • (2021)A Survey on Sentiment Analysis and Opinion Mining in Greek Social MediaInformation10.3390/info1208033112:8(331)Online publication date: 18-Aug-2021
    • (2021)The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining ApplicationsData10.3390/data60500526:5(52)Online publication date: 17-May-2021
    • (2021)Just-in-Time Sentiment Analysis for Streamed Data in GreekNext-Gen Digital Services. A Retrospective and Roadmap for Service Computing of the Future10.1007/978-3-030-73203-5_19(249-263)Online publication date: 10-Apr-2021
    • (2020)NLP for the Greek Language: A Brief Survey11th Hellenic Conference on Artificial Intelligence10.1145/3411408.3411410(101-109)Online publication date: 2-Sep-2020
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