Applied Intelligence

, Volume 48, Issue 5, pp 1218–1232 | Cite as

Hybrid sentiment classification on twitter aspect-based sentiment analysis

  • Nurulhuda Zainuddin
  • Ali Selamat
  • Roliana Ibrahim


Social media sites and applications, including Facebook, YouTube, Twitter and blogs, have become major social media attractions today. The huge amount of information from this medium has become an attractive resource for organisations to monitor the opinions of users, and therefore, it is receiving a lot of attention in the field of sentiment analysis. Early work on sentiment analysis approached this problem at a document-level, where the overall sentiment was identified, rather than the details of the sentiment. This research took into account the use of an aspect-based sentiment analysis on Twitter in order to perform a finer-grained analysis. A new hybrid sentiment classification for Twitter is proposed by embedding a feature selection method. A comparison of the accuracy of the classification by the principal component analysis (PCA), latent semantic analysis (LSA), and random projection (RP) feature selection methods are presented in this paper. Furthermore, the hybrid sentiment classification was validated using Twitter datasets to represent different domains, and the evaluation with different classification algorithms also demonstrated that the new hybrid approach produced meaningful results. The implementations showed that the new hybrid sentiment classification was able to improve the accuracy performance from the existing baseline sentiment classification methods by 76.55, 71.62 and 74.24%, respectively.


Aspect-based sentiment classification Feature selection Principal component analysis Support vector machine Hybrid approach 



The authors wish to thank Universiti Teknologi Malaysia (UTM) under Research University Grant Vot- 02G31, and the Ministry of Higher Education Malaysia (MOHE) under the Fundamental Research Grant Scheme (FRGS Vot-4F551) for the completion of the research.


  1. 1.
    Abbasi A (2010) Intelligent feature selection for opinion classification. IEEE Intell Syst 25(4):75–79. Google Scholar
  2. 2.
    Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R (2011) Sentiment analysis of twitter data. In: Proceedings of the Workshop on Languages in Social Media, LSM ’11. Association for Computational Linguistics, Stroudsburg, pp 30–38Google Scholar
  3. 3.
    Akhtar MS, Gupta D, Ekbal A, Bhattacharyya P (2017) Feature selection and ensemble construction: A two-step method for aspect based sentiment analysis. Knowl-Based Syst 125:116–135. CrossRefGoogle Scholar
  4. 4.
    Appel O, Chiclana F, Carter J, Fujita H (2016) A hybrid approach to the sentiment analysis problem at the sentence level. Knowl-Based Syst 108:110–124. New Avenues in Knowledge Bases for Natural Language ProcessingCrossRefGoogle Scholar
  5. 5.
    Baccianella S, Esuli A, Sebastiani F (2010) Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Calzolari N, Choukri K, Maegaard B, Mariani J, Odijk J, Piperidis S, Rosner M, Tapias D (eds) LREC. European Language Resources Association.
  6. 6.
    Bagheri A, Saraee M, de Jong F (2013) Care more about customers: unsupervised domain-independent aspect detection for sentiment analysis of customer reviews. Knowl-Based Syst 52(0):201–213CrossRefGoogle Scholar
  7. 7.
    Brychcin T, Konkol M, Steinberger J (2014) Uwb: machine learning approach to aspect-based sentiment analysis. SemEval 2014:817Google Scholar
  8. 8.
    Burnap P, Williams ML (2015) Cyber hate speech on twitter: an application of machine classification and statistical modeling for policy and decision making. Policy Internet 7(2):223–242. CrossRefGoogle Scholar
  9. 9.
    De Marneffe MC, Manning CD (2008) The stanford typed dependencies representation. In: Coling 2008: Proceedings of the Workshop on Cross-Framework and Cross-Domain Parser Evaluation, pp 1–8. Association for Computational LinguisticsGoogle Scholar
  10. 10.
    Feldman R (2013) Techniques and applications for sentiment analysis. Commun ACM 56(4):82–89CrossRefGoogle Scholar
  11. 11.
    Ghiassi M, Skinner J, Zimbra D (2013) Twitter brand sentiment analysis: a hybrid system using n-gram analysis and dynamic artificial neural network. Expert Syst Appl 40(16):6266–6282CrossRefGoogle Scholar
  12. 12.
    Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. CS224n Project Report, Stanford, pp 1–12Google Scholar
  13. 13.
    Hu M, Liu B (2004) Mining opinion features in customer reviews. In: Proceedings of the 19th National Conference on Artifical Intelligence, AAAI’04. AAAI Press, pp 755–760Google Scholar
  14. 14.
    Huang G, Huang GB, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48. CrossRefzbMATHGoogle Scholar
  15. 15.
    Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42(2):513–529CrossRefGoogle Scholar
  16. 16.
    Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. In: Nédellec C, Rouveirol C (eds) Machine learning: ECML-98, Lecture Notes in Computer Science, vol 1398. Springer, Berlin, pp 137–142Google Scholar
  17. 17.
    Kansal H, Toshniwal D (2014) Aspect based summarization of context dependent opinion words. Procedia Comput Sci 35(0):166–175. Knowledge-Based and Intelligent Information & Engineering Systems 18th Annual Conference, KES-2014 Gdynia, Poland, September 2014 ProceedingsCrossRefGoogle Scholar
  18. 18.
    Lek HH, Poo D (2013) Aspect-based twitter sentiment classification. In: 2013 IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI), pp 366–373Google Scholar
  19. 19.
    Li S, Zhou L, Li Y (2015) Improving aspect extraction by augmenting a frequency-based method with web-based similarity measures. Inf Process Manag 51(1):58–67CrossRefGoogle Scholar
  20. 20.
    Li Y, Qin Z, Xu W, Guo J (2015) A holistic model of mining product aspects and associated sentiments from online reviews. Multimed Tool Appl 74(23):10177CrossRefGoogle Scholar
  21. 21.
    Liu B (2012) Sentiment analysis and opinion mining. Morgan & Claypool Publishers, San RafaelGoogle Scholar
  22. 22.
    Liu KL, Li WJ, Guo M Emoticon smoothed language models for twitter sentiment analysis. pp 1678–1684 (2012). Cited By (since 1996)1Google Scholar
  23. 23.
    Marrese-Taylor E, Velasquez JD, Bravo-Marquez F (2014) A novel deterministic approach for aspect-based opinion mining in tourism products reviews. Expert Syst Appl 41(17):7764– 7775CrossRefGoogle Scholar
  24. 24.
    Miller GA (1995) Wordnet: a lexical database for english. Commun ACM 38(11):39–41. CrossRefGoogle Scholar
  25. 25.
    Ou G, Chen W, Liu P, Wang T, Yang D, Lei K, Liu Y (2013) Aspect-specific polarity-aware summarization of online reviews. In: Wang J, Xiong H, Ishikawa Y, Xu J, Zhou J (eds) Web-Age Information Management, Lecture Notes in Computer Science, vol 7923. Springer, Berlin, pp 289–300Google Scholar
  26. 26.
    Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing - volume 10, EMNLP ’02. Association for Computational Linguistics, Stroudsburg, pp 79–86Google Scholar
  27. 27.
    Prabowo R, Thelwall M (2009) Sentiment analysis: a combined approach. J Inform 3(2):143–157. CrossRefGoogle Scholar
  28. 28.
    Sabbah T, Selamat A, Selamat MH, Ibrahim R, Fujita H (2016) Hybridized term-weighting method for dark web classification. Neurocomputing 173(3):1908–1926CrossRefGoogle Scholar
  29. 29.
    Saif H, Fernandez M, He Y, Alani H (2013) Evaluation datasets for twitter sentiment analysis: a survey and a new dataset the sts-goldGoogle Scholar
  30. 30.
    Selamat A, Omatu S (2004) Web page feature selection and classification using neural networks. Inf Sci 158:69–88MathSciNetCrossRefGoogle Scholar
  31. 31.
    Tripathy A, Agrawal A, Rath SK (2016) Classification of sentiment reviews using n-gram machine learning approach. Expert Syst Appl 57:117–126. CrossRefGoogle Scholar
  32. 32.
    Vinodhini G, Chandrasekaran MR (2014) Opinion mining using principal component analysis based ensemble model for e-commerce application. CSI Trans on ICT 2(3):169–179CrossRefGoogle Scholar
  33. 33.
    Vinodhini G, Chandrasekaran R (2016) A comparative performance evaluation of neural network based approach for sentiment classification of online reviews. J King Saud University - Comput Inform Sci 28(1):2–12. Google Scholar
  34. 34.
    Yu W, Zhuang F, He Q, Shi Z (2015) Learning deep representations via extreme learning machines. Neurocomputing 149, Part A:308–315. Advances in Neural Networks Advances in Extreme Learning Machines Selected papers from the Tenth International Symposium on Neural Networks (ISNN 2013) Selected articles from the International Symposium on Extreme Learning Machines (ELM 2013)CrossRefGoogle Scholar
  35. 35.
    Zainuddin N, Selamat A (2014) Sentiment analysis using support vector machine. In: 2014 International Conference on Computer, Communications, and Control Technology (i4CT), pp 333–337Google Scholar
  36. 36.
    Zainuddin N, Selamat A, Ibrahim R (2016) Improving twitter aspect-based sentiment analysis using hybrid approach. Springer, Berlin, pp 151–160Google Scholar
  37. 37.
    Ziegelmayer D, Schrader R (2012) Sentiment polarity classification using statistical data compression models. In: 2012 IEEE 12Th International Conference on Data Mining Workshops., pp 731–738

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Nurulhuda Zainuddin
    • 1
  • Ali Selamat
    • 1
    • 2
    • 3
  • Roliana Ibrahim
    • 1
  1. 1.Faculty of ComputingUniversiti Teknologi Malaysia (UTM)Johor BahruMalaysia
  2. 2.MAGIC-X (Media and Game Innovation Centre of Excellence)Universiti Teknologi Malaysia (UTM)Johor BahruMalaysia
  3. 3.Centre for Basic and Applied Research, Faculty of Informatics and ManagementUniversity of Hradec KraloveHradec KraloveCzech Republic

Personalised recommendations