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Sentiment Analysis for Streams of Web Data: A Case Study of Brazilian Financial Markets

Authors:

Bruna Neuenschwander,

Adriano C.M. Pereira,

Wagner Meira, Jr.,

Denilson BarbosaAuthors Info & Claims

WebMedia '14: Proceedings of the 20th Brazilian Symposium on Multimedia and the Web

Pages 167 - 170

https://doi.org/10.1145/2664551.2664579

Published: 18 November 2014 Publication History

Abstract

With the rise of Web 2.0 applications, most people started consuming information and sharing opinions and ideas about most aspects of their lives on a variety of social media platforms, creating massive and continuous streams of valuable data. While this opened the door for information extraction and mining techniques that can help us understand different aspects of society, extracting useful information from such streams of Web data is far from trivial. In this setting, sentiment analysis techniques can be convenient as they are capable of summarizing general feeling about entities people care about, such as products and companies. Therefore, they can be quite applicable in scenarios like the stock market, which also has tremendous impact on society. This paper describes and evaluates two different techniques for sentiment analysis applied to the Brazilian stock market data: lexicon-based and machine learning based, considering a wide range of text pre-processing and feature selection approaches.

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Cited By

Berkin AAerts WVan Caneghem T(2023)Feasibility analysis of machine learning for performance-related attributional statementsInternational Journal of Accounting Information Systems10.1016/j.accinf.2022.10059748(100597)Online publication date: Mar-2023
https://doi.org/10.1016/j.accinf.2022.100597
Vianna DCarneiro FCarvalho JPlastino APaes A(2023)Sentiment analysis in Portuguese tweets: an evaluation of diverse word representation modelsLanguage Resources and Evaluation10.1007/s10579-023-09661-458:1(223-272)Online publication date: 28-Jun-2023
https://doi.org/10.1007/s10579-023-09661-4
Rogers DPreece AInnes MSpasic I(2022)Real-Time Text Classification of User-Generated Content on Social Media: Systematic ReviewIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.31201389:4(1154-1166)Online publication date: Aug-2022
https://doi.org/10.1109/TCSS.2021.3120138
Show More Cited By

Index Terms

Sentiment Analysis for Streams of Web Data: A Case Study of Brazilian Financial Markets
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
  2. Machine learning

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Published In

cover image ACM Other conferences

WebMedia '14: Proceedings of the 20th Brazilian Symposium on Multimedia and the Web

November 2014

256 pages

ISBN:9781450332309

DOI:10.1145/2664551

General Chair:
Raoni Kulesza
UFPB - Universidade Federal da Paraíba
,
Program Chair:
Tatiana Tavares
UFPB - Universidade Federal da Paraíba

Copyright © 2014 ACM.

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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SBC: Brazilian Computer Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 November 2014

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WebMedia'14

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SBC

WebMedia'14: 20th Brazilian Symposium on Multimedia and the Web

November 18 - 21, 2014

João Pessoa, Brazil

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WebMedia '14 Paper Acceptance Rate 25 of 86 submissions, 29%;

Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

Berkin AAerts WVan Caneghem T(2023)Feasibility analysis of machine learning for performance-related attributional statementsInternational Journal of Accounting Information Systems10.1016/j.accinf.2022.10059748(100597)Online publication date: Mar-2023
https://doi.org/10.1016/j.accinf.2022.100597
Vianna DCarneiro FCarvalho JPlastino APaes A(2023)Sentiment analysis in Portuguese tweets: an evaluation of diverse word representation modelsLanguage Resources and Evaluation10.1007/s10579-023-09661-458:1(223-272)Online publication date: 28-Jun-2023
https://doi.org/10.1007/s10579-023-09661-4
Rogers DPreece AInnes MSpasic I(2022)Real-Time Text Classification of User-Generated Content on Social Media: Systematic ReviewIEEE Transactions on Computational Social Systems10.1109/TCSS.2021.31201389:4(1154-1166)Online publication date: Aug-2022
https://doi.org/10.1109/TCSS.2021.3120138
Silva RPaula AMostaço GCosta ACugnasca C(2022)Soybean Price Trend Forecast Using Deep Learning Techniques Based on Prices and Text SentimentsInformation and Communication Technologies for Agriculture—Theme II: Data10.1007/978-3-030-84148-5_10(235-266)Online publication date: 18-Mar-2022
https://doi.org/10.1007/978-3-030-84148-5_10
de O. Carosia ACoelho GSilva Ados Santos JMuchaluat Saade Dda Graça C. Pimentel MMacedo A(2019)The influence of tweets and news on the brazilian stock market through sentiment analysisProceedings of the 25th Brazillian Symposium on Multimedia and the Web10.1145/3323503.3349564(385-392)Online publication date: 29-Oct-2019
https://dl.acm.org/doi/10.1145/3323503.3349564
Martins RAlmeida JNovais PHenriques PNeto MNovais RFerraz CViana W(2018)Creating a social media-based personal emotional lexiconProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3264668(261-264)Online publication date: 16-Oct-2018
https://dl.acm.org/doi/10.1145/3243082.3264668
Justino Garcia Praciano BCarvalho Lustosa da Costa JAbreu Maranhao JLopes de Mendonca Fde Sousa Junior RBarbosa Prettz J(2018)Spatio-Temporal Trend Analysis of the Brazilian Elections Based on Twitter Data2018 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2018.00192(1355-1360)Online publication date: Nov-2018
https://doi.org/10.1109/ICDMW.2018.00192

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