Handling Dimensionality of Ambiguity Using Ensemble Classification in Social Networks to Detect Multi-Label Sentiment Polarity

  • Sudarshan S. Sonawane Kavayitri Bahinabai Chaudhari North Maharashtra University, Jalgaon, India
  • Satish R. Kolhe Kavayitri Bahinabai Chaudhari North Maharashtra University, Jalgaon, India

Abstract

With ever-increasing demand, social media platforms are rapidly developing to enable users to express and share their opinions on a variety of topics. Twitter is one such social media site. This platform enables a comprehensive view of the social media target setting, which may include products, social events, political scenarios, and administrative resolutions. The accessible tweets expressing the target audience’s perspective are frequently impacted by ambiguity caused by natural language processing (NLP) limitations. By classifying tweets according to their sentiment polarity, we can determine whether they express a good or negative point of view, a neutral opinion, or an input tweet that is irrelevant to the sentiment polarity context. Categorizing tweets according to their sentiment can assist future activities within the target domain in constructively evaluating the sentiment polarity and enabling improved decision-making based on the observed sentiment polarity. In this study, tweets that were previously categorized with one of the sentiment polarities were used to conduct predictive analytics of the new tweet to determine its sentiment polarity. The ambiguity of the tweets corpus utilized in the training phase is a critical limitation of the sentiment categorization procedure. While several recent models proposed sentiment classification algorithms, they confined themselves to two labels: positive and negative opinion, oblivious to the plague of ambiguity in the training corpus. In this regard, a novel multi-label classification of sentiment polarity called handling dimensionality of ambiguity using ensemble classification (HAD-EC) method, which diffuses ambiguity and thus minimizes false alerts, is proposed. The experimental assessment validates the HAD-EC approach by comparing the suggested model’s performance to other two existing models.

Keywords

sentiment analysis, ambiguity, fuzzy c-means, NLP, sentiment polarity, Twitter sentiment,

References

1. S. Wiegreffe, A. Marasovic, Teach me to explain: A review of datasets for explainable natural language processing, [in:] Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks, 23 pages, 2021, https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/698d51a19d8a121ce581499d7b701668-Paper-round1.pdf.
2. Y. Belinkov, S. Gehrmann, E. Pavlick, Interpretability and analysis in neural NLP, [in:] Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts, pp. 1–5, 2020, doi: 10.18653/v1/2020.acl-tutorials.1.
3. S.S. Sonawane, S.R. Kolhe, Feature optimization in sentiment analysis by term cooccurrence fitness evolution (TCFE), International Journal of Information Technology and Web Engineering (IJITWE), 14(3): 16–36, 2019, doi: 10.4018/IJITWE.2019070102.
4. S.S. Sonawane, S.R. Kolhe, Term co-occurrence based feature selection for sentiment classification, [in:] P. Bhattacharyya, H. Sastry, V. Marriboyina, R. Sharma [Eds.], Smart and Innovative Trends in Next Generation Computing Technologies, NGCT-2017, Communications in Computer and Information Science, Springer, Vol. 827, pp. 405–417, 2018, doi: 10.1007/978-981-10-8657-1_31.
5. S.M. Rezaeinia, R. Rouhollah, A. Ghodsi, H. Veisi, Sentiment analysis based on improved pre-trained word embeddings, Expert Systems with Applications, 117: 139–147, 2019, doi: 10.1016/j.eswa.2018.08.044.
6. F. Ali et al., Transportation sentiment analysis using word embedding and ontologybased topic modeling, Knowledge-Based Systems, 174: 27–42, 2019, doi: 10.1016/j.knosys.2019.02.033.
7. A. Onan, Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks, Concurrency and Computation: Practice and Experience, 33(23): e5909, 2020, doi: 10.1002/cpe.5909.
8. C.S.G. Khoo, S.B. Johnkhan, Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons, Journal of Information Science, 44(4): 491–511, 2018, doi: 10.1177/0165551517703514.
9. S. Bandari, V.V. Bulusu, Survey on ontology-based sentiment analysis of customer reviews for products and services, [in:] K. Raju, R. Senkerik, S. Lanka, V. Rajagopal [Eds.], Data Engineering and Communication Technology, Advances in Intelligent Systems and Computing, Springer, Vol. 1079, pp. 91–101, 2020, doi: 10.1007/978-981-15-1097-7_8.
10. C. Song, X.-K. Wang, P.-F. Cheng, J.-Q. Wang, L. Li, SACPC: A framework based on probabilistic linguistic terms for short text sentiment analysis, Knowledge-Based Systems, 194, 2020, doi: 10.1016/j.knosys.2020.105572.
11. W. Hua, Z.Wang, H.Wang, K. Zheng, X. Zhou, Understand short texts by harvesting and analyzing semantic knowledge, IEEE Transaction on Knowledge and Data Engineering, 29(3): 499–512, 2017, doi: 10.1109/TKDE.2016.2571687.
12. T. Lee, Z. Wang, H. Wang, S.-W. Hwang, Attribute extraction and scoring: A probabilistic approach, [in:] 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp. 194–205, Brisbane, QLD, Australia, April 8–12, 2013, doi: 10.1109/ICDE.2013.6544825.
13. A. D’Andrea, F. Ferri, P. Grifoni, T. Guzzo, Approaches, tools and applications for sentiment analysis implementation, International Journal of Computer Applications, 125(3): 26–33, 2015, doi: 10.5120/ijca2015905866.
14. A. Hassan, A. Abbasi, D. Zeng, Twitter sentiment analysis: A bootstrap ensemble framework, [in:] 2013 IEEE International Conference on Social Computing, pp. 357–364, Alexandria, VA, USA, September 8–14, 2013, doi: 10.1109/SocialCom.2013.56.
15. M.H. Krishna, K. Rahamathulla, A. Akbar, A feature based approach for sentiment analysis using SVM and coreference resolution, [in:] IEEE International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, March 10–11, 2017, doi: 10.1109/ICICCT.2017.7975227.
16. A. Tripathy, A. Agrawal, S.K. Rath, Classification of sentiment reviews using n-gram machine learning approach, Expert Systems with Applications, 57: 117–126, 2016, doi: 10.1016/j.eswa.2016.03.028.
17. D. Zhang, H. Xu, Z. Su, Y. Xu, Chinese comments sentiment classification based on word2vec and SVMperf, Expert Systems with Applications, 42(4): 1857–1863, 2015, doi: 10.1016/j.eswa.2014.09.011.
18. B. Luo, J. Zeng, J. Duan, Emotion space model for classifying opinions in stock message board, Expert Systems with Applications, 44: 138–146, 2016, doi: 10.1016/j.eswa.2015.08.023.
19. H. Kang, S.J. Yoo, D. Han, Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews, Expert System Applications, 39(5): 6000–6010, 2012, doi: 10.1016/j.eswa.2011.11.107.
20. P. Ray, A. Chakrabarti, Twitter sentiment analysis for product review using lexicon method, [in:] 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), pp. 211–216, Pune, India, February 24–26, 2017, doi: 10.1109/ICDMAI.2017.8073512.
21. S.S. Sonawane, S.R. Kolhe, A new method for defining scale to estimate the aspects oriented sentiment polarity of the tweets, [in]: K.C. Santosh, B. Gawali [Eds.], Recent Trends in Image Processing and Pattern Recognition (RTIP2R) 2020, Communications in Computer and Information Science (CCIS), Springer, Vol. 1380, pp. 318–333, 2021, doi: 10.1007/978-981-16-0507-9_28.
22. S. Deng, A.P. Sinha, H. Zhao, Resolving ambiguity in sentiment classification: The role of dependency features, ACM Transactions on Management Information Systems (TMIS), 8(2–3): 13 pages, 2017, doi: 10.1145/3046684.
23. M. Trupthi, S. Pabboju, G. Narsimha, Possibilistic fuzzy C-means topic modelling for Twitter sentiment analysis, International Journal of Intelligent Engineering and Systems, 11(3): 100–108, 2018, doi: 10.22266/ijies2018.0630.11.
24. Apple Twitter Sentiment, online: https://data.world/crowdflower/apple-twitter-sentiment, accessed on 21.10.2021.
Published
Sep 6, 2022
How to Cite
SONAWANE, Sudarshan S.; KOLHE, Satish R.. Handling Dimensionality of Ambiguity Using Ensemble Classification in Social Networks to Detect Multi-Label Sentiment Polarity. Computer Assisted Methods in Engineering and Science, [S.l.], v. 30, n. 1, p. 7–26, sep. 2022. ISSN 2956-5839. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/471>. Date accessed: 28 mar. 2024. doi: http://dx.doi.org/10.24423/cames.471.
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Articles