Skip to main content
SpringerLink
Log in

Social Network Opinion Mining and Sentiment Analysis: Classification Approaches, Trends, Applications and Issues

  • Conference paper
  • First Online:
Congress on Intelligent Systems (CIS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1334))

Included in the following conference series:

Abstract

Sentiment Analysis (SA) or Opinion Mining (OM) probes the content stated about a topic of interest and classifies them as +ve, −ve or neutral hold on the person's opinion, emotion, judgment articulated in it. Nowadays people’s opinions and reviews on blogs, forums, review websites, E-commerce sites are most vital information in the process decision-making for both organization and users. SA is an information revelation and knowledge discovery technique inherited from data mining. The manual investigation of such immense number of reviews is actually unthinkable so computerized frameworks are expected to do analysis and accordingly classify of information to take related decisions. In general, SA attempts to make some decisions on the base of an author standpoint that may be his or her judgment, mood or evaluation. In this study, we have described the essential concepts, various methods, trends, applications and key issues of OMSA in social networks. We tried to analyze SA literatures from various perspectives and presented those techniques and methods in a systematic manner. With brief descriptions of the algorithms, different SA taxonomical techniques classified along with their advantages and limitations. The critical opportunities that lie ahead for SA are defined and addressed on the basis of those sources. We attempted to present the recent applications and challenging issues in Sentiment Identification and presents evaluation metrics for SA.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

Similar content being viewed by others

References

  1. Batra, R., Daudpota, S.M.: Integrating StockTwits with sentiment analysis for better prediction of stock price movement. In iCoMET, pp. 1–5 (2018)

    Google Scholar 

  2. Baydogan, C., Alatas, B.: Sentiment analysis using Konstanz information miner in social networks (2018), pp. 1–5

    Google Scholar 

  3. Bhatia, S., Sharma, M., Bhatia, K.K.: Internet of Things and Big Data Analytics Toward Next Generation Intelligence, Studies in Big Data, vol. 30 (2018)

    Google Scholar 

  4. Bing, L.: Sentiment analysis and subjectivity. In: Handbook of Natural Language Processing, 2nd edn. Synthesis Lectures on Human Language Technologies. Morgan and Claypool Publisher (2010)

    Google Scholar 

  5. Branz, L., Brockmann, P.: Sentiment analysis of twitter data: towards filtering, analyzing and interpreting social network data. In: DEBS, pp. 238–241. ACM (2018)

    Google Scholar 

  6. Daniel, J.S.O., Paulo, H.S.B., José, R.P., Daniely, A.B.: The application of the sentiment analysis technique in social media as a tool for social management practices at the governmental level. Braz. J. Publ. Adm. 53(1), 235–251 (2017)

    Google Scholar 

  7. Dragoni, M.: Computational advertising in social networks: an opinion mining-based approach. In: Proceedings of ACM SAC Conference, pp. 1798–1804 (2018)

    Google Scholar 

  8. Estrada, M.L.B., Cabada, R.Z., Bustillos, R.O., Graff, M.: Opinion mining and emotion recognition applied to learning environments. Expert Syst. Appl. 113265 (2020)

    Google Scholar 

  9. Haddi, E.: Sentiment analysis: text pre-processing, reader views and cross domains. Dissertation, Brunel University London (2015)

    Google Scholar 

  10. Hamouda, A., Marei, M., Rohaim, M.: Building machine learning based senti-word lexicon for sentiment analysis. J. Adv. Inf. Technol. 2(4), 199–203 (2011)

    Google Scholar 

  11. Hao, F., Park, D., Pei, Z.: When social computing meets soft computing: opportunities and insights. Hum. Centric Comput. Inf. Sci. 8, 8 (2018)

    Article  Google Scholar 

  12. Hogenboom, A.: Sentiment analysis of text guided by semantics and structure. Dissertation, Erasmus University Rotterdam (2015)

    Google Scholar 

  13. Hu, Y.: Text mining and data information analysis for network public opinion. Data Sci. J. 18(7), 1–8 (2019)

    Google Scholar 

  14. Hua, W., Wang, Z., Wang, H, Zheng, K., Zhou, X.: Understand short texts by harvesting and analyzing semantic knowledge. IEEE Trans. Knowl. Data Eng. 29(3), 499–512 (2017)

    Google Scholar 

  15. Indra, Winarko, E., Pulungan, R.: Trending topics detection of Indonesian tweets using BN-grams and Doc-p. J. King Saud Univ. CIS (2018)

    Google Scholar 

  16. Imane, E.A., Gahi, Y., Messoussi, R., Chaabi, Y., Todoskoff, A., Kobi, A.: A novel adaptable approach for sentiment analysis on big social data. J. Big Data 5, 12 (2018)

    Article  Google Scholar 

  17. Jain, D.K., Kumar, A., Sharma, V.: Tweet recommender model using adaptive neuro-fuzzy inference system. Futur. Gener. Comput. Syst. 5571, 1–24 (2020)

    Google Scholar 

  18. Joshi, A., Balamurali, A.R., Bhattacharyya, P., Mohanty, R.: C-feel-it: a sentiment analyzer for micro-blogs. In: The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA, pp. 127–132 (2011)

    Google Scholar 

  19. Kotelnikov, E.V.: TextJSM: text sentiment analysis method. Autom. Documentation Math Linguist. 52(1), 24–34 (2018)

    Article  Google Scholar 

  20. Li, G., Liu, F.: A clustering-based approach on sentiment analysis. In: International Conference on Intelligent Systems and Knowledge Engineering, Hangzhou, pp. 331–337 (2010)

    Google Scholar 

  21. Li, M., Yang, C., Zhang, J., Puthal, D., Luo, Y., Li, J.: Stock market analysis using social networks. ACM (2018)

    Google Scholar 

  22. Mishra, A.: https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithm-f10ba6e38234. Last accessed 2020/04/10

  23. Mohammad, E.M., Mohammad, S.A., Hamidreza, K.: Aspect-based sentiment analysis using adaptive aspect-based lexicons. In: Expert Systems with Applications (2020)

    Google Scholar 

  24. Pathak, A.R., Agarwal, B., Pandey, M., Rautaray, S.: Application of deep learning approaches for sentiment analysis. In: Deep Learning-Based Approaches for Sentiment Analysis. Algorithms for Intelligent Systems, pp. 1–31 (2020)

    Google Scholar 

  25. Pavlopoulos, I.: Aspect based sentiment analysis. Dissertation, Department of Informatics, Athens University of Economics and Business (2014)

    Google Scholar 

  26. Pimpalkar, A.: Review of online product using rule based and fuzzy logic with smileys. Int. J. Comput. Technol. 1(1), 39–44 (2014)

    Google Scholar 

  27. Pimpalkar, A.: A sentimental analysis of movie reviews involving fuzzy rule-based. Int. J. Artif. Intell. Knowl. Discov. 3(2), 9–14 (2013)

    Google Scholar 

  28. Poecze, F., Ebster, C., Strauss, C.: Social media metrics and sentiment analysis to evaluate the effectiveness of social media posts. Procedia Comput. Sci. 660–666 (2018)

    Google Scholar 

  29. Pozzi, F.A., Fersini, E., Messina, E., Liu, B.: Challenges of sentiment analysis in social networks. In: Sentiment Analysis in Social Networks, pp. 1–11 (2017)

    Google Scholar 

  30. Rahman, L., Sarowar, G., Kamal, S.: Teenagers sentiment analysis from social network data. In: Social Networks Science: Design, Implementation, Security, and Challenges, pp. 3–23 (2018)

    Google Scholar 

  31. Rani, R., Lobiyal, D.K.: Performance evaluation of text-mining models with Hindi stopwords lists. J. King Saud Univ. CIS (2020)

    Google Scholar 

  32. Rashid, A., Asif, S., Butt, N.A., Ashraf, I.: Feature level opinion mining of educational student feedback data using sequential pattern mining and association rule mining. Int. J. Comput. Appl. 81(10) (2013)

    Google Scholar 

  33. Ruz, G.A., Henriquez, P.A., Mascareno, A.: Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers. Future Gener. CS (2020)

    Google Scholar 

  34. Seghouani, N.B., Bugiotti, F., Hewasinghage, M., Isaj, S., Quercini, G.: A frequent named entities-based approach for interpreting reputation in Twitter. Data Sci. Eng. 3, 86–100 (2018)

    Article  Google Scholar 

  35. Shayaa, S., Jaafar, N.I., Bahri, S., Sulaiman, A., Wai, P.S., Chung, Y.W., Piprani, A.Z., Ali, M., Garadi, A.: Sentiment analysis of big data: methods, applications, and open challenges 1–26 (2018)

    Google Scholar 

  36. Stylianidis, M., Galiotou, E., Sgouropoulou, C., Skourlas, C.: Opinion mining using an LVQ neural network. In: Proceedings of the 21st Pan-Hellenic Conference on Informatics, Article 61, pp. 1–5 (2017)

    Google Scholar 

  37. Sukthanker, R., Poria, S., Cambria, E., Thirunavukarasu, R.: Anaphora and coreference resolution: a review. Inf. Fusion 59, 139–162 (2019)

    Article  Google Scholar 

  38. Tang, B., Kay, S., He, H.: Toward optimal feature selection in Naïve Bayes for text categorization. IEEE Trans. Knowl. Data Eng. 28(9), 2508–2521 (2016)

    Article  Google Scholar 

  39. Wang, J., Dong, A.: A comparison of two text representations for sentiment analysis. In: International Conference on Computer Application and System Modelling, pp. 35–39 (2010)

    Google Scholar 

  40. Wataru, S., Irena, V., Hideaki, A.: Enhanced news sentiment analysis using deep learning methods. J. Comput. Soc. Sci. 2, 33–46 (2019)

    Article  Google Scholar 

  41. Zhou, X., Tao, X., Rahman, M.M., Zhang, J.: Coupling topic modelling in opinion mining for social media analysis. In: Proceedings of International Conference on Web Intelligence, pp. 533–540 (2017)

    Google Scholar 

  42. Zulfadzli, D., Haliyana, K.: Sentiment analysis in social media and its application: systematic literature review. Procedia Comput. Sci. 161, 707–714 (2019). (Fifth Information Systems International Conference)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing, Sathyabama Institute of Science and Technology, Chennai, India

    Amit Pimpalkar & R. Jeberson Retna Raj

Authors
  1. Amit Pimpalkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Jeberson Retna Raj
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Mukesh Saraswat

  3. National Institute of Technology, Jalandhar, Punjab, India

    Anupam Yadav

  4. Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  5. South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

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

Pimpalkar, A., Jeberson Retna Raj, R. (2021). Social Network Opinion Mining and Sentiment Analysis: Classification Approaches, Trends, Applications and Issues. In: Sharma, H., Saraswat, M., Yadav, A., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. CIS 2020. Advances in Intelligent Systems and Computing, vol 1334. Springer, Singapore. https://doi.org/10.1007/978-981-33-6981-8_60

Download citation

Publish with us

Policies and ethics

Search

Navigation