IDEAS home Printed from https://ideas.repec.org/a/eee/ininma/v44y2019icp1-12.html
   My bibliography  Save this article

Towards a big data framework for analyzing social media content

Author

Listed:
  • Jimenez-Marquez, Jose Luis
  • Gonzalez-Carrasco, Israel
  • Lopez-Cuadrado, Jose Luis
  • Ruiz-Mezcua, Belen

Abstract

Modern companies generate value by digitalizing their services and products. Knowing what customers are saying about the firm through reviews in social media content constitutes a key factor to succeed in the big data era. However, social media data analysis is a complex discipline due to the subjectivity in text review and the additional features in raw data. Some frameworks proposed in the existing literature involve many steps that thereby increase their complexity. A two-stage framework to tackle this problem is proposed: the first stage is focused on data preparation and finding an optimal machine learning model for this data; the second stage relies on established layers of big data architectures focused on getting an outcome of data by taking most of the machine learning model of stage one. Thus, a first stage is proposed to analyze big and small datasets in a non-big data environment, whereas the second stage analyzes big datasets by applying the first stage machine learning model of. Then, a study case is presented for the first stage of the framework to analyze reviews of hotel-related businesses. Several machine learning algorithms were trained for two, three and five classes, with the best results being found for binary classification.

Suggested Citation

  • Jimenez-Marquez, Jose Luis & Gonzalez-Carrasco, Israel & Lopez-Cuadrado, Jose Luis & Ruiz-Mezcua, Belen, 2019. "Towards a big data framework for analyzing social media content," International Journal of Information Management, Elsevier, vol. 44(C), pages 1-12.
    • Handle: RePEc:eee:ininma:v:44:y:2019:i:c:p:1-12
    DOI: 10.1016/j.ijinfomgt.2018.09.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0268401218305073
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijinfomgt.2018.09.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bayramusta, Merve & Nasir, V. Aslihan, 2016. "A fad or future of IT?: A comprehensive literature review on the cloud computing research," International Journal of Information Management, Elsevier, vol. 36(4), pages 635-644.
    2. Ghaddar, Bissan & Naoum-Sawaya, Joe, 2018. "High dimensional data classification and feature selection using support vector machines," European Journal of Operational Research, Elsevier, vol. 265(3), pages 993-1004.
    3. Rong, Jia & Vu, Huy Quan & Law, Rob & Li, Gang, 2012. "A behavioral analysis of web sharers and browsers in Hong Kong using targeted association rule mining," Tourism Management, Elsevier, vol. 33(4), pages 731-740.
    4. Sultan, Nabil, 2013. "Cloud computing: A democratizing force?," International Journal of Information Management, Elsevier, vol. 33(5), pages 810-815.
    5. Larson, Deanne & Chang, Victor, 2016. "A review and future direction of agile, business intelligence, analytics and data science," International Journal of Information Management, Elsevier, vol. 36(5), pages 700-710.
    6. Yaqoob, Ibrar & Hashem, Ibrahim Abaker Targio & Gani, Abdullah & Mokhtar, Salimah & Ahmed, Ejaz & Anuar, Nor Badrul & Vasilakos, Athanasios V., 2016. "Big data: From beginning to future," International Journal of Information Management, Elsevier, vol. 36(6), pages 1231-1247.
    7. Lin, Angela & Chen, Nan-Chou, 2012. "Cloud computing as an innovation: Percepetion, attitude, and adoption," International Journal of Information Management, Elsevier, vol. 32(6), pages 533-540.
    8. Rehman, Muhammad Habib ur & Chang, Victor & Batool, Aisha & Wah, Teh Ying, 2016. "Big data reduction framework for value creation in sustainable enterprises," International Journal of Information Management, Elsevier, vol. 36(6), pages 917-928.
    9. Li, Xin & Pan, Bing & Law, Rob & Huang, Xiankai, 2017. "Forecasting tourism demand with composite search index," Tourism Management, Elsevier, vol. 59(C), pages 57-66.
    10. Xu, Xun & Wang, Xuequn & Li, Yibai & Haghighi, Mohammad, 2017. "Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors," International Journal of Information Management, Elsevier, vol. 37(6), pages 673-683.
    11. Hu, Ya-Han & Chen, Kuanchin, 2016. "Predicting hotel review helpfulness: The impact of review visibility, and interaction between hotel stars and review ratings," International Journal of Information Management, Elsevier, vol. 36(6), pages 929-944.
    12. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    13. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    14. Law, Rob & Rong, Jia & Vu, Huy Quan & Li, Gang & Lee, Hee Andy, 2011. "Identifying changes and trends in Hong Kong outbound tourism," Tourism Management, Elsevier, vol. 32(5), pages 1106-1114.
    15. Deng, Ning & Li, Xiang (Robert), 2018. "Feeling a destination through the “right” photos: A machine learning model for DMOs’ photo selection," Tourism Management, Elsevier, vol. 65(C), pages 267-278.
    16. Costa, Carlos & Santos, Maribel Yasmina, 2017. "The data scientist profile and its representativeness in the European e-Competence framework and the skills framework for the information age," International Journal of Information Management, Elsevier, vol. 37(6), pages 726-734.
    17. Senyo, Prince Kwame & Addae, Erasmus & Boateng, Richard, 2018. "Cloud computing research: A review of research themes, frameworks, methods and future research directions," International Journal of Information Management, Elsevier, vol. 38(1), pages 128-139.
    18. Raguseo, Elisabetta, 2018. "Big data technologies: An empirical investigation on their adoption, benefits and risks for companies," International Journal of Information Management, Elsevier, vol. 38(1), pages 187-195.
    19. Ahmad, Shimi Naurin & Laroche, Michel, 2017. "Analyzing electronic word of mouth: A social commerce construct," International Journal of Information Management, Elsevier, vol. 37(3), pages 202-213.
    20. Lin, Xiaolin & Li, Yibai & Wang, Xuequn, 2017. "Social commerce research: Definition, research themes and the trends," International Journal of Information Management, Elsevier, vol. 37(3), pages 190-201.
    21. Lismont, Jasmien & Vanthienen, Jan & Baesens, Bart & Lemahieu, Wilfried, 2017. "Defining analytics maturity indicators: A survey approach," International Journal of Information Management, Elsevier, vol. 37(3), pages 114-124.
    22. Xiang, Zheng & Du, Qianzhou & Ma, Yufeng & Fan, Weiguo, 2017. "A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism," Tourism Management, Elsevier, vol. 58(C), pages 51-65.
    23. Yuan, Hua & Xu, Hualin & Qian, Yu & Li, Yan, 2016. "Make your travel smarter: Summarizing urban tourism information from massive blog data," International Journal of Information Management, Elsevier, vol. 36(6), pages 1306-1319.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Papoutsoglou, Maria & Rigas, Emmanouil S. & Kapitsaki, Georgia M. & Angelis, Lefteris & Wachs, Johannes, 2022. "Online labour market analytics for the green economy: The case of electric vehicles," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    2. Ghouri, Arsalan Mujahid & Mani, Venkatesh & Haq, Mirza Amin ul & Kamble, Sachin S., 2022. "The micro foundations of social media use: Artificial intelligence integrated routine model," Journal of Business Research, Elsevier, vol. 144(C), pages 80-92.
    3. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. Hyun, Hyowon & Thavisay, Toulany & Lee, Suk Hyung, 2022. "Enhancing the role of flow experience in social media usage and its impact on shopping," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Acharya, Abhilash & Singh, Sanjay Kumar & Pereira, Vijay & Singh, Poonam, 2018. "Big data, knowledge co-creation and decision making in fashion industry," International Journal of Information Management, Elsevier, vol. 42(C), pages 90-101.
    2. Gupta, Shivam & Kar, Arpan Kumar & Baabdullah, Abdullah & Al-Khowaiter, Wassan A.A., 2018. "Big data with cognitive computing: A review for the future," International Journal of Information Management, Elsevier, vol. 42(C), pages 78-89.
    3. Ariyaluran Habeeb, Riyaz Ahamed & Nasaruddin, Fariza & Gani, Abdullah & Targio Hashem, Ibrahim Abaker & Ahmed, Ejaz & Imran, Muhammad, 2019. "Real-time big data processing for anomaly detection: A Survey," International Journal of Information Management, Elsevier, vol. 45(C), pages 289-307.
    4. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    5. Carmela Iorio & Giuseppe Pandolfo & Antonio D’Ambrosio & Roberta Siciliano, 2020. "Mining big data in tourism," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(5), pages 1655-1669, December.
    6. Tursunbayeva, Aizhan & Di Lauro, Stefano & Pagliari, Claudia, 2018. "People analytics—A scoping review of conceptual boundaries and value propositions," International Journal of Information Management, Elsevier, vol. 43(C), pages 224-247.
    7. Chae, Bongsug (Kevin), 2019. "A General framework for studying the evolution of the digital innovation ecosystem: The case of big data," International Journal of Information Management, Elsevier, vol. 45(C), pages 83-94.
    8. Ashrafi, Amir & Zare Ravasan, Ahad & Trkman, Peter & Afshari, Samira, 2019. "The role of business analytics capabilities in bolstering firms’ agility and performance," International Journal of Information Management, Elsevier, vol. 47(C), pages 1-15.
    9. Lim, Chiehyeon & Kim, Ki-Hun & Kim, Min-Jun & Heo, Jun-Yeon & Kim, Kwang-Jae & Maglio, Paul P., 2018. "From data to value: A nine-factor framework for data-based value creation in information-intensive services," International Journal of Information Management, Elsevier, vol. 39(C), pages 121-135.
    10. Falana, Gbenga Ayodele & Olusola Esther (PhD) & Dagunduro, Muyiwa Emmanuel, 2023. "Effect of Big Data on Accounting Information Quality in Selected Firms in Nigeria," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 7(3), pages 789-806, March.
    11. Carvalho, João Vidal & Rocha, Álvaro & Vasconcelos, José & Abreu, António, 2019. "A health data analytics maturity model for hospitals information systems," International Journal of Information Management, Elsevier, vol. 46(C), pages 278-285.
    12. Calvard, Thomas Stephen & Jeske, Debora, 2018. "Developing human resource data risk management in the age of big data," International Journal of Information Management, Elsevier, vol. 43(C), pages 159-164.
    13. Yang, Bai & Liu, Ying & Liang, Yan & Tang, Min, 2019. "Exploiting user experience from online customer reviews for product design," International Journal of Information Management, Elsevier, vol. 46(C), pages 173-186.
    14. Yucheng Zhang & Zhiling Wang & Lin Xiao & Lijun Wang & Pei Huang, 2023. "Discovering the evolution of online reviews: A bibliometric review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    15. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    16. Sunyoung Hlee & Hanna Lee & Chulmo Koo, 2018. "Hospitality and Tourism Online Review Research: A Systematic Analysis and Heuristic-Systematic Model," Sustainability, MDPI, vol. 10(4), pages 1-27, April.
    17. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.
    18. Wenzhi Cao & Xingen Yang & Yi Yang, 2023. "A Large-Scale Reviews-Driven Multi-Criteria Product Ranking Approach Based on User Credibility and Division Mechanism," Mathematics, MDPI, vol. 11(13), pages 1-19, July.
    19. Tosporn Arreeras & Mikiharu Arimura & Takumi Asada & Saharat Arreeras, 2019. "Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data," Sustainability, MDPI, vol. 11(14), pages 1-17, July.
    20. Saito, Taiga & Takahashi, Akihiko & Koide, Noriaki & Ichifuji, Yu, 2019. "Application of online booking data to hotel revenue management," International Journal of Information Management, Elsevier, vol. 46(C), pages 37-53.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ininma:v:44:y:2019:i:c:p:1-12. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-information-management .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.