IDEAS home Printed from https://ideas.repec.org/a/igg/jsda00/v10y2021i4p1-12.html
   My bibliography  Save this article

Sentiment Analysis of Multilingual Tweets Based on Natural Language Processing (NLP)

Author

Listed:
  • Abhijit Bera

    (OmDayal Group of Institutions, India)

  • Mrinal Kanti Ghose

    (GLA University, Mathura, India)

  • Dibyendu Kumar Pal

    (Asansol Engineering College (AEC), India)

Abstract

Multilingual Sentiment analysis plays an important role in a country like India with many languages as the style of expression varies in different languages. The Indian people speak in total 22 different languages and with the help of Google Indic keyboard people can express their sentiments i.e reviews about anything in the social media in their native language from individual smart phones. It has been found that machine learning approach has overcome the limitations of other approaches. In this paper, a detailed study has been carried out based on Natural Language Processing (NLP) using Simple Neural Network (SNN) ,Convolutional Neural Network(CNN), and Long Short Term Memory (LSTM)Neural Network followed by another amalgamated model adding a CNN layer on top of the LSTM without worrying about versatility of multilingualism. Around 4000 samples of reviews in English, Hindi and in Bengali languages are considered to generate outputs for the above models and analyzed. The experimental results on these realistic reviews are found to be effective for further research work.

Suggested Citation

  • Abhijit Bera & Mrinal Kanti Ghose & Dibyendu Kumar Pal, 2021. "Sentiment Analysis of Multilingual Tweets Based on Natural Language Processing (NLP)," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(4), pages 1-12, October.
    • Handle: RePEc:igg:jsda00:v:10:y:2021:i:4:p:1-12
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSDA.20211001.oa16
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Milton M. Herrera & Lina A. Carvajal-Prieto & Mauricio Uriona-Maldonado & Fernando Ojeda, 2019. "Modeling the Customer Value Generation in the Industry's Supply Chain," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 8(4), pages 1-13, October.
    2. Qingliang Miao & Qiudan Li & Daniel Zeng, 2010. "Fine‐grained opinion mining by integrating multiple review sources," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(11), pages 2288-2299, November.
    3. Sana Bouzaida & Anis Sakly, 2018. "Adaptive Neuro-Fuzzy Sliding Mode Controller," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 7(2), pages 34-54, April.
    4. Qingliang Miao & Qiudan Li & Daniel Zeng, 2010. "Fine-grained opinion mining by integrating multiple review sources," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(11), pages 2288-2299, November.
    5. Prabowo, Rudy & Thelwall, Mike, 2009. "Sentiment analysis: A combined approach," Journal of Informetrics, Elsevier, vol. 3(2), pages 143-157.
    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. Drazen Draskovic & Darinka Zecevic & Bosko Nikolic, 2022. "Development of a Multilingual Model for Machine Sentiment Analysis in the Serbian Language," Mathematics, MDPI, vol. 10(18), pages 1-17, September.

    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. Hossain, Md Shamim & Rahman, Mst Farjana, 2022. "Detection of potential customers’ empathy behavior towards customers' reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    2. Zunqiang Zhang & Guoqing Chen & Jin Zhang & Xunhua Guo & Qiang Wei, 2016. "Providing Consistent Opinions from Online Reviews: A Heuristic Stepwise Optimization Approach," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 236-250, May.
    3. Saba Resnik & Mateja Kos Koklič, 2018. "User-Generated Tweets about Global Green Brands: A Sentiment Analysis Approach," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 30(2), pages 125-145.
    4. Pashchenko, Yana & Rahman, Mst Farjana & Hossain, Md Shamim & Uddin, Md Kutub & Islam, Tarannum, 2022. "Emotional and the normative aspects of customers’ reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    5. Perez-Cepeda, Maximiliano & Arias-Bolzmann, Leopoldo G., 2022. "Sociocultural factors during COVID-19 pandemic: Information consumption on Twitter," Journal of Business Research, Elsevier, vol. 140(C), pages 384-393.
    6. Shuyue Huang & Lena Jingen Liang & Hwansuk Chris Choi, 2022. "How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures," Sustainability, MDPI, vol. 14(5), pages 1-18, February.
    7. Yucel, Ahmet & Dag, Ali & Oztekin, Asil & Carpenter, Mark, 2022. "A novel text analytic methodology for classification of product and service reviews," Journal of Business Research, Elsevier, vol. 151(C), pages 287-297.
    8. Shivendra Kumar & C. Ravindranath Chowdary, 2022. "Semantic model to extract tips from hotel reviews," Electronic Commerce Research, Springer, vol. 22(4), pages 1059-1077, December.
    9. F. Schweitzer & D. Garcia, 2010. "An agent-based model of collective emotions in online communities," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(4), pages 533-545, October.
    10. Yaxin Bi, 2022. "Sentiment classification in social media data by combining triplet belief functions," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(7), pages 968-991, July.
    11. Nan Jing & Tao Jiang & Juan Du & Vijayan Sugumaran, 2018. "Personalized recommendation based on customer preference mining and sentiment assessment from a Chinese e-commerce website," Electronic Commerce Research, Springer, vol. 18(1), pages 159-179, March.
    12. Xiangfeng Luo & Yawen Yi, 2019. "Topic-Specific Emotion Mining Model for Online Comments," Future Internet, MDPI, vol. 11(3), pages 1-18, March.
    13. Barış-Tüzemen Özge & Tüzemen Samet & Çelik Ali Kemal, 2023. "Sentiment analysis of reviews on cappadocia: The land of beautiful horses in the eyes of tourists," European Journal of Tourism, Hospitality and Recreation, Sciendo, vol. 13(2), pages 188-197, December.
    14. A. Geethapriya & S. Valli, 2021. "An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment Analysis," Information Systems Frontiers, Springer, vol. 23(3), pages 791-805, June.
    15. C. Sarai R. Avila, 2024. "Tweet Influence on Market Trends: Analyzing the Impact of Social Media Sentiment on Biotech Stocks," Papers 2402.03353, arXiv.org.
    16. Tran, Anh D. & Pallant, Jason I. & Johnson, Lester W., 2021. "Exploring the impact of chatbots on consumer sentiment and expectations in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    17. Cristescu Marian Pompiliu & Nerişanu Raluca Andreea & Mara Dumitru Alexandru, 2022. "Using Data Mining in the Sentiment Analysis Process on the Financial Market," Journal of Social and Economic Statistics, Sciendo, vol. 11(1-2), pages 36-58, December.
    18. Giacomo Manetti & Carmela Nitti & Marco Bellucci, 2022. "The accountability of Search and Rescue NGOs," Working Papers - Business wp2022_02.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    19. Youngseok Choi & Habin Lee, 0. "Data properties and the performance of sentiment classification for electronic commerce applications," Information Systems Frontiers, Springer, vol. 0, pages 1-20.
    20. Cristina Franciele & Thays Christina Domareski Ruiz, 2021. "Using sentiment analysis in tourism research: A systematic, bibliometric, and integrative review," Post-Print hal-03373984, HAL.

    More about this item

    Statistics

    Access and download statistics

    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:igg:jsda00:v:10:y:2021:i:4: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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.