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2018 | OriginalPaper | Chapter

Big Data Analytics on Twitter

A Systematic Review of Applications and Methods

Authors : Mudit Pradyumn, Akshat Kapoor, Nasseh Tabrizi

Published in: Big Data – BigData 2018

Publisher: Springer International Publishing

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Abstract

As the amount of digital data is growing at an exponential rate, the emphasis is on forming an insight from the data. Although the new fields of research, including Twitter data analytics, are proven to be fruitful, there is a lack of literature review and classification of the research. Therefore, after segregating 1,025 research papers, we reviewed 29 papers from 20 journals on Twitter data analytics published from 2011 to 2017, and then classified them based on year of publication, the title of journals, data mining methods, and their application. This paper is written with the intent of understanding the trend of research in this field.

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previous chapter Design and Application of a Visual System for the Supply Chain of Thermal Coal Based on Big Data
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Literature
1.
2.
3.
4.
5.
6.
Durahim, A.O., Co, M.: Technological forecasting & social change #iamhappybecause : gross national happiness through twitter analysis and big data, vol. 99, pp. 92–105 (2015)
7.
Dea, B.O., Wan, S., Batterham, P.J., Calear, A.L., Paris, C., Christensen, H.: Detecting suicidality on Twitter. Invent 2(2), 183–188 (2015)
8.
Yu, Y., Wang, X.: Computers in human behavior world cup 2014 in the twitter world: a big data analysis of sentiments in U.S. sports fans’ tweets. Comput. Human Behav. 48, 392–400 (2015)CrossRef
9.
10.
11.
Crannell, W.C., Clark, E., Jones, C., James, T.A., Moore, J.: Sciencedirect association for academic surgery a pattern-matched twitter analysis of us cancer-patient sentiments. J. Surg. Res. 206(2), 536–542 (2016)CrossRef
12.
13.
Bravo-marquez, F., Frank, E., Pfahringer, B.: Knowledge-based systems building a twitter opinion lexicon from automatically-annotated tweets, vol. 108, pp. 65–78 (2016)CrossRef
14.
Corea, F.: Can twitter proxy the investors’ sentiment? the case for the technology sector. Big Data Res. 4, 70–74 (2016)CrossRef
15.
16.
Schumaker, R.P., Jarmoszko, A.T., Jr, C.S.L.: Predicting wins and spread in the premier league using a sentiment analysis of twitter. Decis. Support Syst. 88, 76–84 (2016)CrossRef
17.
Daniel, M., Neves, R.F., Horta, N.: Company event popularity for financial markets using twitter and sentiment analysis. Expert Syst. Appl. 71, 111–124 (2016)CrossRef
18.
Pandey, A.C., Rajpoot, D.S., Saraswat, M.: Twitter sentiment analysis using hybrid cuckoo search method. Inf. Process. Manag. 53(4), 764–779 (2017)CrossRef
19.
Oliveira, N., Cortez, P., Areal, N.: The impact of microblogging data for stock market prediction: using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Syst. Appl. 73, 125–144 (2017)CrossRef
20.
Huang, Y., Guo, D., Kasakoff, A., Grieve, J.: Understanding U.S. regional linguistic variation with Twitter data. Comput. Environ. Urban Syst. 59, 244–255 (2016)CrossRef
21.
Sulis, E., Irazú, D., Farías, H., Rosso, P., Patti, V.: Knowledge-Based Systems Figurative messages and affect in Twitter : Differences between #irony, #sarcasm and #not, vol. 108, pp. 132–143 (2016)
22.
Oussalah, M., Escallier, B., Daher, D.: An automated system for grammatical analysis of Twitter messages. a learning task application. Knowl. Based Syst. 101, 31–47 (2015)CrossRef
23.
Lahuerta-Otero, E., Cordero-Gutirrez, R.: Looking for the perfect tweet. the use of data mining techniques to find influencers on twitter. Comput. Hum. Behav. 64, 575–583 (2016)CrossRef
24.
25.
26.
Park, C.S., Kaye, B.K.: The tweet goes on: interconnection of twitter opinion leadership, network size, and civic eng. Comput. Hum. Behav. 69, 174–180 (2017)CrossRef
27.
Carley, K.M., Malik, M., Landwehr, P.M., Pfeffer, J., Kowalchuck, M.: Crowd sourcing disaster management: the complex nature of twitter usage in padang Indonesia. Saf. Sci. 90, 48–61 (2016)CrossRef
28.
Communities, W.B.: Twitter for crisis communication : lessons learned from Japan’ s tsunami disaster Adam Acar * and Yuya Muraki, vol. 7(3), pp. 392–402 (2011)
29.
Gu, Y., Sean, Z., Chen, F.: From Twitter to detector: real-time traffic incident detection using social media data. Transp. Res. Part C 67, 321–342 (2016)CrossRef
30.
31.
Laylavi, F., Rajabifard, A., Kalantari, M.: Event relatedness assessment of twitter messages for emergency respons. Inf. Process. Manag. 53(1), 266–280 (2015)CrossRef
32.
Lin, X., Lachlan, K.A., Spence, P.R.: Computers in human behavior exploring extreme events on social media: A comparison of user reposting/retweeting behaviors on Twitter and Weibo. Comput. Human Behav. 65, 576–581 (2016)CrossRef
33.
34.
35.
36.
Daniulaityte, R., et al.: ‘ Time for dabs’: analyzing twitter data on marijuana concentrates across the U. S. Drug Alcohol Depend. 155, 307–311 (2015)CrossRef
37.
Kayser, V., Bierwisch, A.: Using twitter for foresight: an opportunity? Futures 84, 50–63 (2016)CrossRef
38.
Achrekar, H., Lazarus, R., Park, W.C.: Predicting Flu Trends using Twitter Data, pp. 702–707 (2011)
Metadata
Title
Big Data Analytics on Twitter
Authors
Mudit Pradyumn
Akshat Kapoor
Nasseh Tabrizi
Copyright Year
2018
Book
Big Data – BigData 2018

Print ISBN: 978-3-319-94300-8

Electronic ISBN: 978-3-319-94301-5

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-94301-5_26

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