Skip to main content

A Novel Proof of Concept for Twitter Analytics Using Popular Hashtags: Experimentation and Evaluation

  • Conference paper
  • First Online:
Proceedings of International Conference on Communication and Artificial Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 192))

Abstract

Twitter analytics is a classic research area especially with the widespread presence of Big Data in various online media such as—social network sites, online portals for shopping, e-commerce, forums, chats, recommendation systems, and online services. Ascertaining the sentiment behind, the various types of tweets by different persons can provide great insights on various aspects including behavioral patterns. Besides highlighting the newest trends in the field, we retrieved real-time twitter data pertaining to three currently popular hashtags in the Indian context and carried out extensive experimentation analysis about the prevailing sentiment of a strata of population. Inclusion of current challenges, future trends and applications of sentiment analysis from Twitter data makes this novel work very useful for fellow researchers.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Institutional subscriptions

References

  1. Kumar R, Vadlamani R (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge Based Systems

    Google Scholar 

  2. Liu B (2010) Sentiment analysis: a multi-faceted problem. IEEE Intell Syst 25(3):76–80

    Google Scholar 

  3. Tubishat M, Idris N, Abushariah MAM (2018) Implicit aspect extraction in sentiment analysis: review, taxonomy, opportunities, and open challenges. Inf Process Manage 54:545–563

    Article  Google Scholar 

  4. Montoyo A, Martínez-Barco P, Balahur A (2012) Subjectivity and sentiment analysis: an overview of the current state of the area and envisaged developments. Decis Support Syst 53:675–679

    Article  Google Scholar 

  5. Kumar S, Morstatter F, Liu H (2013) Twitter data analytics. Springer

    Google Scholar 

  6. Liu J (2008) Opinion spam and analysis. In: Proceedings of the international conference on web search and web data mining, ACM

    Google Scholar 

  7. Tan LKW, Na JC, Theng YL et al (2012) Phrase-level sentiment polarity classification using rule-based typed dependencies and additional complex phrases consideration. J Comput Sci Technol 27(3):650–666

    Google Scholar 

  8. Wang T et al (2014) Product aspect extraction supervised with online domain knowledge. Knowledge-Based Syst 71:86–100

    Google Scholar 

  9. Kunte AV, Panicker S (2020) Analysis of machine learning algorithms for predicting personality: brief survey and experimentation. In: 2019 global conference for advancement in technology (GCAT)

    Google Scholar 

  10. Kunte A, Panicker S (2020) Personality prediction of social network users using ensemble and XGBoost. In: Das H, Pattnaik P, Rautaray S, Li KC (eds) Progress in computing, analytics and networking. Advances in intelligent systems and computing, vol 1119. Springer, Singapore

    Google Scholar 

  11. Kunte AV, Panicker SS (2019) Using textual data for personality prediction: a machine learning approach. In: 2019 4th international conference on information systems and computer networks (ISCON)

    Google Scholar 

  12. Panicker S, Kunte A (2019) Personality prediction using social media. In: 2019 5th international conference for convergence in technology (I2CT), Pune (in Press)

    Google Scholar 

  13. Mane VL, Panicker SS (2015) Knowledge discovery from user health posts. In: IEEE 9th international conference on intelligent systems and control (ISCO)

    Google Scholar 

  14. Mane VL, Panicker SS (2015) Summarization and sentiment analysis from user health posts. In: 2015 international conference on pervasive computing (ICPC). IEEE

    Google Scholar 

  15. Salunke V, Panicker SS (2021) Image sentiment analysis using deep learning. In: Ranganathan G, Chen J, Rocha A (eds) Inventive communication and computational technologies. Lecture notes in networks and systems, vol 145. Springer, Singapore

    Google Scholar 

  16. Dangra BS, Rajput D, Bedekar MV, Panicker SS (2015) Profiling of automobile drivers using car games. In: International conference on pervasive computing (ICPC). IEEE

    Google Scholar 

  17. Bedekar MV, Atote B, Zahoor S, Panicker S (2016) Proposed used of information DisPersal Algorithm in user profiling. In: International conference on ICT for sustainable development, Goa, India

    Google Scholar 

  18. Khan M, Malviya A (2020) Big data approach for sentiment analysis of twitter data using Hadoop framework and deep learning. In: 2020 international conference on emerging trends in information technology and engineering (ic-ETITE), Vellore, India

    Google Scholar 

  19. Hu T, She B, Duan L, Yue H, Clunis J (2020) A systematic spatial and temporal sentiment analysis on geo-tweets. IEEE Access 8, 8658–8667

    Google Scholar 

  20. Murakami A, Nasukawa T, Watanabe K, Hatayama M (2020) Understanding requirements and issues in disaster area using geotemporal visualization of Twitter analysis. IBM J Res Develop

    Google Scholar 

  21. Kumar TS, Nabeem PM, Manoj CK, Jeyachandran K (2020) Sentimental analysis (opinion mining) in social network by using SVM algorithm. In: 2020 fourth international conference on computing methodologies and communication (ICCMC), Erode, India

    Google Scholar 

  22. Phan HT, Tran VC, Nguyen NT, Hwang D (2020) Improving the performance of sentiment analysis of Tweets containing fuzzy sentiment using the feature ensemble model. In: IEEE Access, vol 8, pp 14630–14641

    Google Scholar 

  23. Bhatnagar D, SubaLakshmi RJ, Vanmathi C (2020) Twitter Sentiment Analysis Using Elasticsearch, LOGSTASH And KIBANA. In: 2020 international conference on emerging trends in information technology and engineering (ic-ETITE)

    Google Scholar 

  24. Oyasor J, Raborife M, Ranchod P (2020) Sentiment analysis as an indicator to evaluate gender disparity on sexual violence tweets in South Africa. In: 2020 international SAUPEC/RobMech/PRASA conference, Cape Town, South Africa

    Google Scholar 

  25. Joshi PA, Simon G, Murumkar YP (2018) Generation of brand/product reputation using Twitter data. In: 2018 international conference on information, communication, engineering and technology (ICICET), Pune

    Google Scholar 

  26. Wang W, Li B, Feng D, Zhang A, Wan S (2020) The OL-DAWE model: tweet polarity sentiment analysis with data augmentation. In: IEEE Access, vol 8, pp 40118–40128

    Google Scholar 

  27. Li YM, Shiu YL (2012) A diffusion mechanism for social advertising over microblogs. Decis Support Syst 54:9–22

    Article  Google Scholar 

  28. Du J et al (2013) Box office prediction based on microblog. In: Expert systems with applications

    Google Scholar 

  29. Al-Moslmi T, Omar N, Abdullah S, Albared M (2017) Approaches to cross-domain sentiment analysis: a systematic literature review. IEEE Access 5:16173–16192

    Article  Google Scholar 

  30. Li SK, Guan Z, Tang LY et al (2012) Exploiting consumer reviews for product feature ranking. J Comput Sci Technol 27(3):635–649

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Suja Sreejith Panicker .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ahire, K., Bagul, M., Dhanawate, S., Panicker, S.S. (2021). A Novel Proof of Concept for Twitter Analytics Using Popular Hashtags: Experimentation and Evaluation. In: Goyal, V., Gupta, M., Trivedi, A., Kolhe, M.L. (eds) Proceedings of International Conference on Communication and Artificial Intelligence. Lecture Notes in Networks and Systems, vol 192. Springer, Singapore. https://doi.org/10.1007/978-981-33-6546-9_31

Download citation

  • DOI: https://doi.org/10.1007/978-981-33-6546-9_31

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-6545-2

  • Online ISBN: 978-981-33-6546-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics