Skip to main content
SpringerLink
Log in
Book cover

International Conference on Soft Computing in Data Science

SCDS 2019: Soft Computing in Data Science pp 375–385Cite as

Sentiment Analysis in Social Media Based on English Language Multilingual Processing Using Three Different Analysis Techniques

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1100))

Abstract

Numerous numbers of companies have utilized the web to offer their services and products. Web customers dependably look through the reviews of other customers towards a product or service before they chose to buy the things or viewed the films. The company needs to analyse their customers’ sentiment and feeling based on their comments. The outcome of the sentiment analysis makes the companies easily discover either the expression of their users is more to positive or negative. There are numerous numbers of sentiment analysis techniques available in the market today. However, only three (3) techniques will be used in this research which are the Python NLTK Text Classification, Miopia and MeaningCloud. These techniques used to analyse the sentiment analysis of the reviews and comments from English language in social media. 2400 datasets from Amazon, Kaggle, IMdB, and Yelp were used to analyse the accuracy of these techniques. From this analyses, average accuracy for sentiment analysis using Python NLTK Text Classification is 74.5%, meanwhile only 73% accuracy achieved using Miopia technique. The accuracy achieved when using MeaningCloud technique is 82.1% which is the highest compared to other techniques. This shows that hybrid technique offers a greatest accuracy for sentiment analysis on social reviews.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bhuta, S., Doshi, A., Doshi, U., Narvekar, M.: A review of techniques for sentiment analysis of twitter data. In: 2014 International Conference on Issues and Challenge in Intelligent Computing Techniques (ICICT) (2014)

    Google Scholar 

  2. Sarlan, A., Nadam, C., Basri, S.: Twitter sentiment analysis. In: 2014 International Conference on Information Technology and Multimedia (ICIMU), Putrajaya, Malaysia (2014)

    Google Scholar 

  3. Cardona-Grau, D., Sorokin, I., Leinwand, G., Welliver, C.: Introducing the Twitter impact factor: an objective measure of urology’s academic impact on Twitter. Eur. Urol. Focus. 2(4), 412–417 (2016)

    Article  Google Scholar 

  4. Dashtipour, K., et al.: Erratum to: multilingual sentiment analysis: state of the art and independent comparison of techniques. Cogn. Comput. 8(4), 772–775 (2016)

    Article  Google Scholar 

  5. Neethu, M.S., Rajasree, R.: Sentiment analysis in Twitter using machine learning techniques. In: 4th ICCCNT, Tiruchengode, India (2013)

    Google Scholar 

  6. Bhavitha, B.K., Anisha, P.R., Chilunkar, N.N.: Comparative study of machine learning techniques in sentimental analysis. In: International Conference on Inventive Communication and Computational Technologies (2017)

    Google Scholar 

  7. Hailong, Z., Wengan, G., Jiang, B.: Machine learning and lexicon based methods for sentiment classification: a survey. In: 11th Web Information System and Application Conference (2014)

    Google Scholar 

  8. Ehsan Basiri, M., Kabiri, A.: Translation is not enough: comparing lexicon-based methods for sentiment analysis in Persian. In: International Symposium on Computer Science and Software Engineering Conference (CSSE) (2017)

    Google Scholar 

  9. Okpor, M.: Machine translation approaches: issues and challenges. Int. J. Comput. Sci. Issues 11(5), 159–165 (2014)

    Google Scholar 

  10. Al-Aidaroos, K., Abu Bakar, A., Othman, Z.: Naive Bayes variants in classification learning. In: International Conference on Information Retrieval & Knowledge Management (CAMP) (2010)

    Google Scholar 

  11. Jadav, B.M., Vaghela, V.B.: Sentiment analysis using support vector machine based on feature selection and semantic analysis. Int. J. Comput. Appl. 146(13) (2016)

    Google Scholar 

  12. Al-Saffar, M., Sabri, B., Tao, H., Awang, S., Abdul Majid, M., Al-Saiagh, W.: Sentiment analysis in arabic social media using association rule mining. J. Eng. Appl. Sci. 11(2), 3239–3247 (2016)

    Google Scholar 

  13. Ang, S.L., Ong, H.C., Low, H.C.: Classification using the general bayesian network. Pertanika J. Sci. & Technol. 24(1), 205–211 (2016)

    Google Scholar 

  14. Minn, S., Shunkai, F., Desmarais, M.C.: Efficient learning of general Bayesian Network Classifier by local and adaptive search. In: International Conference on Data Science and Advanced Analyrics (DSAA) (2014)

    Google Scholar 

  15. Noorhuzaimi, M.N., Ab Aziz, M.J., Mohd Noah, S.A., Hamzah, M.P.: Anaphora resolution of malay text: issues and proposed solution model. In: International Conference on Asian Language Processing (2010)

    Google Scholar 

  16. Ballabh, A., Jaiswal, U.C.: A study of machine translation methods and their challenges. Int. J. Adv. Res. Sci. Eng. 4(1), 423–429 (2015)

    Google Scholar 

  17. Al-Saffar, A., Awang, S., Tao, H., Omar, N., Al-Saiagh, W., Al-Bared, M.: Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm. PLoS ONE 13(4), e0194852 (2018)

    Article  Google Scholar 

  18. Yusof, N.N., Mohamed, A., Abdul-Rahman, S.: Reviewing classification approaches in sentiment analysis. In: Berry, M., Mohamed, A., Yap, B. (eds.) SCDS 2015. CCIS, vol. 545, pp. 43–53. Springer, Singapore (2015). https://doi.org/10.1007/978-981-287-936-3_5

    Chapter  Google Scholar 

  19. Lo, S.L., Cambria, E., Chiong, R., Cornforth, D.: Multilingual sentiment analysis: from formal to informal and scarce resource languages. Artif. Intell. Rev. 48(4), 499–527 (2017)

    Article  Google Scholar 

  20. Foucart, A., Frenck-Mestre, C.: Natural language processing. In: The Cambridge Handbook of Second Language Acquisition, pp. 394–416. https://ieeexplore.ieee.org/document/6678407

Download references

Acknowledgement

A high appreciation to Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, under grant RDU1703287.

Author information

Authors and Affiliations

  1. Soft Computing & Intelligent Systems (SPINT), Faculty of Computing, Universiti Malaysia Pahang, 26300, Gambang, Kuantan, Pahang, Malaysia

    Nor Saradatul Akmar Zulkifli & Allen Wei Kiat Lee

Authors
  1. Nor Saradatul Akmar Zulkifli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Allen Wei Kiat Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nor Saradatul Akmar Zulkifli .

Editor information

Editors and Affiliations

  1. University of Tennessee, Knoxville, TN, USA

    Michael W. Berry

  2. Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Bee Wah Yap

  3. Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Azlinah Mohamed

  4. Kyushu Institute of Technology, Fukuoka, Japan

    Mario Köppen

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zulkifli, N.S.A., Lee, A.W.K. (2019). Sentiment Analysis in Social Media Based on English Language Multilingual Processing Using Three Different Analysis Techniques. In: Berry, M., Yap, B., Mohamed, A., Köppen, M. (eds) Soft Computing in Data Science. SCDS 2019. Communications in Computer and Information Science, vol 1100. Springer, Singapore. https://doi.org/10.1007/978-981-15-0399-3_30

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-0399-3_30

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0398-6

  • Online ISBN: 978-981-15-0399-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation