Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Neural Computing and Applications
Published in: Neural Computing and Applications 8/2020

06-02-2019 | Original Article

Application of soft computing techniques for estimating emotional states expressed in Twitter® time series data

Authors: Erman Çakıt, Waldemar Karwowski, Les Servi

Published in: Neural Computing and Applications | Issue 8/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Because the emotional states of selected social groups may constitute a complex phenomenon, a suitable methodology is needed to analyze Twitter® text data that can reflect social emotions. Understanding the nature of social barometer data in terms of its underlying dynamics is critical for predicting the future states or behaviors of large social groups. This study investigated the use of the supervised soft computing techniques (1) fuzzy time series (FTS), (2) artificial neural network (ANN)-based FTS, and (3) adaptive neuro-fuzzy inference systems (ANFIS) for predicting the emotional states expressed in Twitter® data. The examined dataset contained 25,952 data points reflecting more than 380,000 Twitter® messages recorded hourly. The model prediction accuracy was performed using the root-mean-square error. The ANFIS approach resulted in the most accurate prediction among the three examined soft computing approaches. The findings of the study showed that the FTS, ANN-based FTS, and ANFIS models could be used to predict the emotional states of a large social group based on historical data. Such a modeling approach can support the development of real-time social and emotional awareness for practical decision-making, as well as rapid socio-cultural assessment and training.
previous article Measuring distance-based semantic similarity using meronymy and hyponymy relations
next article A novel online method for identifying motion artifact and photoplethysmography signal reconstruction using artificial neural networks and adaptive neuro-fuzzy inference system

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Prata DN, Soares KP, Silva MA, Trevisan DQ, Letouze P (2016) Social data analysis of Brazilian’s mood from Twitter. Int J Soc Sci Humanit 6(3):179CrossRef
2.
3.
Karwowski W (2012) A review of human factors challenges of complex adaptive systems: discovering and understanding chaos in human performance. Hum Factors 54(6):983–995CrossRef
4.
5.
Pennebaker JW, Boyd RL, Jordan K, Blackburn K (2015) The development and psychometric properties of LIWC2015. University of Texas at Austin, Austin
6.
Pennebaker JW, Booth RE, Francis ME (2007) Linguistic inquiry and word count: LIWC2007—operator’s manual. LIWC.net, Austin
7.
Pennebaker JW, Chung CK, Ireland M, Gonzales A, Booth RJ (2007) The development and psychometric properties of LIWC2007. LIWC.net, Austin
8.
Kumar M, Dredze M, Coppersmith G, De Choudhury M (2015) Detecting changes in suicide content manifested in social media following celebrity suicides. In: 26th ACM conference on hypertext & social media; September 01–04, 2015; Guzelyurt, Northern Cyprus. ACM Press, New York, pp 85–94
9.
10.
De Choudhury M, Gamon M, Counts S, Horvitz E (2013) Predicting depression via social media. In: 7th international AAAI conference on weblogs and social media (ICWSM); July 8–11, 2013, Boston, USA
11.
Wang Y, Kraut R, Levine J (2012) To stay or leave? The relationship of emotional and informational support to commitment in online health support groups. In: ACM 2012 conference on computer supported cooperative work; February 11–15, 2012. ACM Press, Seattle, pp 833–842
12.
Hancock J, Gee K, Ciaccio K, Lin J (2008) I’m sad you’re sad: emotional contagion in CMC. 2008 In: ACM conference on computer supported cooperative work; November 08–12, 2008. ACM Press, San Diego, pp 295–298
13.
Hancock J, Landrigan C, Silver C (2007) Expressing emotion in text-based communication. In: SIGCHI conference on human factors in computing systems; April 28–May 03, 2007. ACM Press, San Jose, CA, pp 929–932
14.
Mogadala A, Varma V (2012) Twitter user behavior understanding with mood transition prediction. In: Proceedings of the 2012 workshop on data-driven user behavioral modelling and mining from social media ACM, pp 31–34
15.
Bing L, Chan KC (2014) A fuzzy logic approach for opinion mining on large scale twitter data. In: Proceedings of the 2014 IEEE/ACM 7th international conference on utility and cloud computing. IEEE Computer Society, pp 652–657
16.
Dash AK, Rout JK, Jena SK (2016) Harnessing twitter for automatic sentiment identification using machine learning techniques. In: Proceedings of 3rd international conference on advanced computing, networking and informatics. Springer, New Delhi, pp 507–514
17.
Wang N, Varghese B, Donnelly PD (2016) A machine learning analysis of Twitter sentiment to the Sandy Hook shootings. arXiv preprint arXiv:​1609.​00536
18.
Huq MR, Ali A, Rahman A (2017) Sentiment analysis on Twitter data using KNN and SVM. Int J Adv Comput Sci Appl 8(6):19–25
19.
Mac Kim S (2011). Recognising emotions and sentiments in text. Ph.D. thesis, University of Sydney
20.
Asur S, Huberman BA (2010). Predicting the future with social media. In: 2010 IEEE/WIC/ACM international conference on, web intelligence and intelligent agent technology (WI-IAT), vol 1. IEEE, pp 492–499
21.
Gilbert E, Karahalios K (2010) Widespread worry and the stock market. In: ICWSM, pp 59–65
22.
Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1–8CrossRef
23.
Chen GH, Nikolov S, Shah D (2013) A latent source model for nonparametric time series classification. In: Advances in neural information processing systems, vol. 26. pp 1088–1096
24.
Ma Z, Sun A, Cong G (2013) On predicting the popularity of newly emerging hashtags in twitter. J Assoc Inf Sci Technol 64(7):1399–1410CrossRef
25.
Kong S, Mei Q, Feng L, Ye F, Zhao Z (2014) Predicting bursts and popularity of hashtags in real-time. In: Proceedings of the 37th international ACM SIGIR conference on research & development in information retrieval. ACM, pp 927–930
26.
Servi LD, Elson SE (2014) A mathematical approach to identifying and forecasting shifts in the mood of social media users. IEEE Trans Comput Soc Syst 1(4):180–190CrossRef
27.
28.
Song Q, Chissom BS (1993) Forecasting enrollments with fuzzy time series—part I. Fuzzy Sets Syst 54:1–10CrossRef
29.
30.
Egrioglu E, Aladag CH, Yolcu U, Uslu VR, Basaran MA (2009) A new approach based on artificial neural networks for high order multivariate fuzzy time series. Expert Syst Appl 36(7):10589–10594CrossRef
31.
Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(5):665–685CrossRef
32.
Çakıt E, Karwowski W, Bozkurt H, Ahram T, Thompson W, Mikusinski P, Lee G (2014) Investigating the relationship between adverse events and infrastructure development in an active war theater using soft computing techniques. Appl Soft Comput 25:204–214CrossRef
33.
Zaheeruddin G (2006) A neuro-fuzzy approach for prediction of human work efficiency in noisy environment. Appl Soft Comput 6:283–294CrossRef
34.
Çakıt E, Karwowski W (2017) Predicting the occurrence of adverse events using an adaptive neuro-fuzzy inference system (ANFIS) approach with the help of ANFIS input selection. Artif Intell Rev 48(2):139–155CrossRef
35.
Nauck D, Klawonn F, Kruse R (1997) Foundations of neuro-fuzzy systems. Wiley, New YorkMATH
36.
Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci Model Dev 7(3):1247–1250CrossRef
37.
Rantala J, Koivisto H (2002) Optimised subtractive clustering for neuro-fuzzy models. In: 3rd WSEAS international conference on fuzzy sets and fuzzy systems
38.
Stavroulakis P (ed) (2004) Neuro-fuzzy and fuzzy-neural applications in telecommunications. Springer, Berlin
39.
Rezaei H, Rahmati M, Modarress H (2017) Application of ANFIS and MLR models for prediction of methane adsorption on X and Y faujasite zeolites: effect of cations substitution. Neural Comput Appl 28(2):301–312CrossRef
40.
Lampos V, Lansdall-Welfare T, Araya R, Cristianini N (2013) Analysing mood patterns in the united kingdom through twitter content. arXiv preprint arXiv:​1304.​5507
Metadata
Title
Application of soft computing techniques for estimating emotional states expressed in Twitter® time series data
Authors
Erman Çakıt
Waldemar Karwowski
Les Servi
Publication date
06-02-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 8/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04048-5

Other articles of this Issue 8/2020

Neural Computing and Applications 8/2020 Go to the issue

Original Article

Decentralized robust optimal control for modular robot manipulators via critic-identifier structure-based adaptive dynamic programming

Original Article

Classification of EEG signals using hybrid combination of features for lie detection

Original Article

Adversarial neural networks for playing hide-and-search board game Scotland Yard

Original Article

Supplier selection based on normal process yield: the Bayesian inference

Original Article

A novel possibility measure to interval-valued intuitionistic fuzzy set using connection number of set pair analysis and its applications

Original Article

Non-fragile robust finite-time stabilization and performance analysis for fractional-order delayed neural networks with discontinuous activations under the asynchronous switching

Premium Partner

    Image Credits