demonstration

iFeel: a system that compares and combines sentiment analysis methods

Authors:

Matheus Araújo,

Pollyanna Gonçalves,

Fabrício BenevenutoAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 75 - 78

https://doi.org/10.1145/2567948.2577013

Published: 07 April 2014 Publication History

Abstract

Sentiment analysis methods are used to detect polarity in thoughts and opinions of users in online social media. As businesses and companies are interested in knowing how social media users perceive their brands, sentiment analysis can help better evaluate their product and advertisement campaigns. In this paper, we present iFeel, a Web application that allows one to detect sentiments in any form of text including unstructured social media data. iFeel is free and gives access to seven existing sentiment analysis methods: SentiWordNet, Emoticons, PANAS-t, SASA, Happiness Index, SenticNet, and SentiStrength. With iFeel, users can also combine these methods and create a new Combined-Method that achieves high coverage and F-measure. iFeel provides a single platform to compare the strengths and weaknesses of various sentiment analysis methods with a user friendly interface such as file uploading, graphical visualizing, and weight tuning.

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Cited By

Müller SSeiler RVölkle M(2023)Should Net Promoter Score be supplemented with other customer feedback metrics? An empirical investigation of Net Promoter Score and emotions in the mobile phone industryInternational Journal of Market Research10.1177/1470785323121964866:2-3(303-320)Online publication date: 5-Dec-2023
https://doi.org/10.1177/14707853231219648
Mabokela KCelik TRaborife M(2023)Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the LandscapeIEEE Access10.1109/ACCESS.2022.322413611(15996-16020)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2022.3224136
Oueslati OHajhmida MOunelli HCambria E(2023)Sentiment Analysis of Influential Messages for Political Election ForecastingComputational Linguistics and Intelligent Text Processing10.1007/978-3-031-24340-0_21(280-292)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-24340-0_21
Show More Cited By

Index Terms

iFeel: a system that compares and combines sentiment analysis methods
1. Applied computing
  1. Law, social and behavioral sciences
2. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

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Information

Published In

cover image ACM Other conferences

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Copyright © 2014 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

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Author Tags

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Demonstration

Funding Sources

Brazilian National Institute of Science and Technology for theWeb (MCT/CNPq/INCT
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Fundação de Amparo ã Pesquisa do Estado de Minas Gerais
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Ministry of Science, ICT and Future Planning

Conference

WWW '14

Sponsor:

IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Citations

Cited By

Müller SSeiler RVölkle M(2023)Should Net Promoter Score be supplemented with other customer feedback metrics? An empirical investigation of Net Promoter Score and emotions in the mobile phone industryInternational Journal of Market Research10.1177/1470785323121964866:2-3(303-320)Online publication date: 5-Dec-2023
https://doi.org/10.1177/14707853231219648
Mabokela KCelik TRaborife M(2023)Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the LandscapeIEEE Access10.1109/ACCESS.2022.322413611(15996-16020)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2022.3224136
Oueslati OHajhmida MOunelli HCambria E(2023)Sentiment Analysis of Influential Messages for Political Election ForecastingComputational Linguistics and Intelligent Text Processing10.1007/978-3-031-24340-0_21(280-292)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-24340-0_21
Aldous KAn JJansen B(2022)Measuring 9 Emotions of News Posts from 8 News Organizations across 4 Social Media Platforms for 8 MonthsACM Transactions on Social Computing10.1145/35164914:4(1-31)Online publication date: 23-Mar-2022
https://dl.acm.org/doi/10.1145/3516491
Viggiato MLin DHindle ABezemer C(2022)What Causes Wrong Sentiment Classifications of Game Reviews?IEEE Transactions on Games10.1109/TG.2021.307254514:3(350-363)Online publication date: Sep-2022
https://doi.org/10.1109/TG.2021.3072545
Subhashini LLi YZhang JAtukorale AWu Y(2021)Mining and classifying customer reviews: a surveyArtificial Intelligence Review10.1007/s10462-021-09955-554:8(6343-6389)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1007/s10462-021-09955-5
Saxena AReddy HSaxena P(2021)Introduction to Sentiment Analysis Covering Basics, Tools, Evaluation Metrics, Challenges, and ApplicationsPrinciples of Social Networking10.1007/978-981-16-3398-0_12(249-277)Online publication date: 19-Aug-2021
https://doi.org/10.1007/978-981-16-3398-0_12
Susino MSchubert E(2020)Musical emotions in the absence of music: A cross-cultural investigation of emotion communication in music by extra-musical cuesPLOS ONE10.1371/journal.pone.024119615:11(e0241196)Online publication date: 18-Nov-2020
https://doi.org/10.1371/journal.pone.0241196
Araújo MPereira ABenevenuto F(2020)A comparative study of machine translation for multilingual sentence-level sentiment analysisInformation Sciences: an International Journal10.1016/j.ins.2019.10.031512:C(1078-1102)Online publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1016/j.ins.2019.10.031
Martins Kde Melo Pdos Santos JMuchaluat Saade Dda Graça C. Pimentel MMacedo A(2019)Characterization of the discrepancies between scores and texts of movie reviewsProceedings of the 25th Brazillian Symposium on Multimedia and the Web10.1145/3323503.3360296(229-236)Online publication date: 29-Oct-2019
https://dl.acm.org/doi/10.1145/3323503.3360296
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