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„Śmieci na wejściu, śmieci na wyjściu”. Wpływ jakości koderów na działanie sieci neuronowej klasyfikującej wypowiedzi w mediach społecznościowych

Paweł Matuszewski
Studia Socjologiczne | 2022 | No 2 | 137-164 | DOI: 10.24425/sts.2022.141426
Keywords sieci neuronowe klasyfikacja danych tekstowych modele nadzorowane opinion mining jakość koderów
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Abstract

Jedna z głównych decyzji przy ręcznym kodowaniu danych tekstowych dotyczy tego, czy kodowanie ma być weryfikowane. W przypadku modeli nadzorowanych prowadzi to do istotnego dylematu: czy lepszym rozwiązaniem jest dostarczenie modelowi dużej liczby przypadków, na których będzie się uczyć kosztem weryfikacji poprawności danych, czy też zakodowanie każdego przypadku n-razy, co pozwoli porównać kody i sprawdzić ich poprawność, ale jednocześnie n-krotnie zmniejszy zbiór danych treningowych. Taka decyzja może zaważyć nie tylko na ostatecznych wynikach klasyfikatora. Z punktu widzenia badaczy jest istotna również dlatego, że – realistycznie zakładając, że badania mają ograniczone źródło finansowania – nie można jej cofnąć. Wykorzystując 100 tys. unikatowych i ręcznie zakodowanych tweetów przeprowadzono symulacje wyników klasyfikatora w zależności od kontrolowanego odsetka błędnie zakodowanych dokumentów. Na podstawie danych przedstawiono rekomendacje.
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Bibliography

1. Ajdukiewicz, Kazimierz. 1965. Logika pragmatyczna. Warszawa: Państwowe Wydawnictwo Naukowe.
2. Anon. 2021. „FastText”. Facebook Research. Pobrano 17 marzec 2021 (https://research.fb.com/downloads/fasttext/).
3. Bai, Qiyu, Qi Dan, Zhe Mu, Maokun Yang. 2019. A Systematic Review of Emoji: Current Research and Future Perspectives. Frontiers in Psychology. DOI: 10.3389/fpsyg.2019.02221.
4. Bail, Christopher A. 2014. The Cultural Environment: Measuring Culture with Big data. Theory and Society, 43, 3: 465–82. DOI: 10.1007/s11186-014-9216-5.
5. Bakliwal, Akshat, Jennifer Foster, Jennifer van der Puil, Ron O’Brien, Lamia Tounsi, Mark Hughes. 2013. Sentiment Analysis of Political Tweets: Towards an Accurate Classifier. In: Proceedings of the NAACL Workshop on Language Analysis in Social Media. Atlanta, GA.: Association for Computational Linguistics.
6. Barberá, Pablo, Amber E. Boydstun, Suzanna Linn, Ryan McMahon, Jonathan Nagler. 2021. Automated Text Classification of News Articles: A Practical Guide. Political Analysis, 29, 1:19–42. DOI: 10.1017/pan.2020.8.
7. Batorski, Dominik, Marta Olcoń-Kubicka. 2006. Prowadzenie badań przez internet – podstawowe zagadnienia metodologiczne. Studia Socjologiczne, 182, 3: 99–132.
8. Batorski, Dominik, Krzysztof Olechnicki. 2007. Wprowadzenie do socjologii internetu. Studia Socjologiczne, 186, 3: 5–14.
9. Boudon, Raymond. 1997. The Art of Self-Persuasion: The Social Explanation of False Beliefs. Cambridge, England; Malden, Mass.: Polity.
10. Brants, Wesley, Bonita Sharif, Alexander Serebrenik. 2019. Assessing the Meaning of Emojis for Emotional Awareness – A Pilot Study. s. 419–23. In: Companion Proceedings of The 2019 World Wide Web Conference, WWW ’19. New York, NY, USA: Association for Computing Machinery.
11. Cha, Meeyoung, Hamed Haddadi, Fabrício Benevenuto, Krishna P. Gummadi. 2010. Measuring user influence in Twitter: The million follower fallacy. In: ICWSM ’10: Proceedings of international AAAI Conference on Weblogs and Social.
12. Chen, Yukun, Subramani Mani, Hua Xu. 2012. Applying Active Learning to Assertion Classification of Concepts in Clinical Text. Journal of Biomedical Informatics, 45, 2: 265–72. DOI: 10.1016/j.jbi.2011.11.003.
13. Denny, Matthew J., Arthur Spirling. 2018. Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It. Political Analysis, 26, 2: 168–89. DOI: 10.1017/pan.2017.44.
14. Di Franco, Giovanni, Michele Santurro. 2020. Machine Learning, Artificial Neural Networks and Social Research. Quality & Quantity. DOI: 10.1007/s11135-02001037-y.
15. DiMaggio, Paul. 2015. Adapting Computational Text Analysis to Social Science (and Vice Versa). Big Data & Society, 2, 2. DOI: 10.1177/2053951715602908.
16. Drus, Zulfadzli, Haliyana Khalid. 2019. Sentiment Analysis in Social Media and Its Application: Systematic Literature Review. Procedia Computer Science, 161: 707–14. DOI: 10.1016/j.procs.2019.11.174.
17. Goldenstein, Jan, Philipp Poschmann. 2019. A Quest for Transparent and Reproducible Text-Mining Methodologies in Computational Social Science. Sociological Methodology, 49, 1: 144–51. DOI: 10.1177/0081175019867855.
18. Grimmer, Justin, Brandon M. Stewart. 2013. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21, 3: 267–97. DOI: 10.1093/pan/mps028.
19. HaCohen-Kerner, Yaakov, Daniel Miller, Yair Yigal. 2020. The Influence of Preprocessing on Text Classification Using a Bag-of-Words Representation. PLOS ONE, 15, 5: e0232525. DOI: 10.1371/journal.pone.0232525.
20. Haddi, Emma, Xiaohui Liu, Yong Shi. 2013. The Role of Text Pre-Processing in Sentiment Analysis. Procedia Computer Science, 17: 26–32. doi: 10.1016/j.procs.2013.05.005.
21. Hand, David J. 2006. Classifier Technology and the Illusion of Progress. Statistical Science, 21, 1: 1–14. DOI: 10.1214/088342306000000060.
22. He, Zhoushanyue, Matthias Schonlau. 2020a. Automatic Coding of Open-Ended Questions into Multiple Classes: Whether and How to Use Double Coded Data. Survey Research Methods, 14, 3: 267–87. DOI: 10.18148/srm/2020.v14i3.7639.
23. He, Zhoushanyue, Matthias Schonlau. 2020b. Automatic Coding of Text Answers to Open-Ended Questions: Should You Double Code the Training Data? Social Science Computer Review, 38, 6: 754–65. DOI: 10.1177/0894439319846622.
24. Hopkins, Daniel J., Gary King. 2010. A Method of Automated Nonparametric Content Analysis for Social Science. American Journal of Political Science, 54, 1: 229–47. DOI: 10.1111/j.1540-5907.2009.00428.x.
25. Ignatow, Gabe. 2016. Theoretical Foundations for Digital Text Analysis. Journal for the Theory of Social Behaviour, 46, 1: 104–20. DOI: 10.1111/jtsb.12086.
26. Jemielniak, Dariusz. 2018. Socjologia 2.0: o potrzebie łączenia Big Data z etnografią cyfrową, wyzwaniach jakościowej socjologii cyfrowej i systematyzacji pojęć. Studia Socjologiczne, 242, 2: 7–29. DOI: 10.24425/122461.
27. Jemielniak, Dariusz. 2019. Socjologia internetu. Warszawa: Wydawnictwo Naukowe Scholar.
28. Jordan, Michael, Tom Mitchell. 2015. Machine Learning: Trends, Perspectives, and Prospects. Science, 349, 6245: 255–60. DOI: 10.1126/science.aaa8415.
29. Joseph, Kenneth, Sarah Shugars, Ryan Gallagher, Jon Green, Alexi Quintana Mathé, Zijian An, David Lazer. 2021. (Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys. arXiv:2109.01762 [cs].
30. Joulin, Armand, Edouard Grave, Piotr Bojanowski, Tomas Mikolov. 2016. Bag of Tricks for Efficient Text Classification. arXiv:1607.01759 [cs].
31. Krippendorff, Klaus H. 2003. Content Analysis: An Introduction to Its Methodology. Thousand Oaks, Calif: Sage Publications, Inc.
32. Lazer, David, Alex (Sandy) Pentland, Lada Adamic, Sinan Aral, Albert Laszlo Barabasi, Devon Brewer, Nicholas Christakis, Noshir Contractor, James Fowler, Myron Gutmann, Tony Jebara, Gary King, Michael Macy, Deb Roy, Marshall Van Alstyne. 2009. Life in the network: the coming age of computational social science. Science, 323, 5915: 721–23. DOI: 10.1126/science.1167742.
33. Lin, Chenghua, Yulan He. 2009. Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM conference on Information and knowledge management, CIKM ’09. New York, NY, USA: Association for Computing Machinery, 375–384.
34. Marciszewski, Witold. 1972. Podstawy logicznej teorii przekonań. Warszawa: Państwowe Wydawnictwo Naukowe.
35. Miller, Blake, Fridolin Linder, Walter R. Mebane. 2020. Active Learning Approaches for Labeling Text: Review and Assessment of the Performance of Active Learning Approaches. Political Analysis, 28, 4: 532–51. DOI: 10.1017/pan.2020.4.
36. Mohammad, Saif M., Parinaz Sobhani, Svetlana Kiritchenko. 2016. Stance and Sentiment in Tweets. arXiv:1605.01655 [cs].
37. Monroe, Burt L. 2019. The Meanings of “Meaning in Social Scientific Text Analysis. Sociological Methodology, 49, 1: 132–39. DOI: 10.1177/0081175019865231.
38. Mozetič, Igor, Miha Grčar, Jasmina Smailović. 2016. Multilingual Twitter Sentiment Classification: The Role of Human Annotators. PLOS ONE 11, 5:e0155036. DOI: 10.1371/journal.pone.0155036.
39. Murthy, Dhiraj, Sawyer A. Bowman. 2014. Big Data Solutions on a Small Scale: Evaluating Accessible High-Performance Computing for Social Research: Big Data & Society. DOI: 10.1177/2053951714559105.
40. Nelson, Laura K. 2019. To Measure Meaning in Big Data, Don’t Give Me a Map, Give Me Transparency and Reproducibility. Sociological Methodology, 49, 1: 139–43. DOI: 10.1177/0081175019863783.
41. Rodak, Olga. 2017. Twitter jako przedmiot badań socjologicznych i źródło danych społecznych: perspektywa konstruktywistyczna. Studia Socjologiczne, 226, 3: 209–36.
42. Salganik, Matthew J. 2017. Bit by Bit: Social Research in the Digital Age. Illustrated edition. Princeton: Princeton University Press.
43. Sobhani, Parinaz, Diana Inkpen, Xiaodan Zhu. 2019. Exploring Deep Neural Networks for Multitarget Stance Detection. Computational Intelligence, 35, 1: 82–97. DOI: 10.1111/coin.12189.
44. Subedi, Nishan. 2018. FastText: Under the Hood. Medium. Pobrano 3 grudzień 2021 (https://towardsdatascience.com/fasttext-under-the-hood-11efc57b2b3).
45. Tharwat, Alaa. 2020. Classification assessment methods. Applied Computing and Informatics ahead-of-print(ahead-of-print). DOI: 10.1016/j.aci.2018.08.003.
46. Tomanek, Krzysztof. 2017. Metodyka dla analizy treści w projektach stosujących techniki text mining i rozwiązania CAQDAS piątej generacji. Przegląd Socjologii Jakościowej, 13, 2: 128–43.
47. Turner, Anna, Marcin W. Zieliński, Kazimierz M. Słomczyński. 2018. Google Big Data: charakterystyka i zastosowanie w naukach społecznych. Studia Socjologiczne, 231, 4: 49–71. DOI: 10.24425/122482.
48. Watts, Duncan J., Peter Sheridan Dodds. 2007. Influentials, Networks, and Public Opinion Formation. Journal of Consumer Research, 34, 4: 441–58. DOI: 10.1086/518527.
49. Wiedemann, Gregor. 2019. Proportional Classification Revisited: Automatic Content Analysis of Political Manifestos Using Active Learning. Social Science Computer Review, 37, 2: 135–59. DOI: 10.1177/0894439318758389.
50. Ziółkowski, Marek. 1989. Wiedza, jednostka, społeczeństwo: zarys koncepcji socjologii wiedzy. Warszawa: Państwowe Wydawnictwo Naukowe.
51. Żulicki, Remigiusz. 2017. Potencjał Big Data w badaniach społecznych. Studia Socjologiczne, 226, 3: 175–207.

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Authors and Affiliations

Paweł Matuszewski
1
ORCID: ORCID
  1. Collegium Civitas

Aspect-based sentiment classification model employing whale-optimized adaptive neural network

Nallathambi Balaganesh K. Muneeswaran
Bulletin of the Polish Academy of Sciences Technical Sciences | 2021 | 69 | 3 | e137271 | DOI: 10.24425/bpasts.2021.137271
Keywords aspect-based sentiment analysis whale optimization algorithm artificial neural network opinion mining
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Abstract

Nowadays in e-commerce applications, aspect-based sentiment analysis has become vital, and every consumer started focusing on various aspects of the product before making the purchasing decision on online portals like Amazon, Walmart, Alibaba, etc. Hence, the enhancement of sentiment classification considering every aspect of products and services is in the limelight. In this proposed research, an aspect-based sentiment classification model has been developed employing sentiment whale-optimized adaptive neural network (SWOANN) for classifying the sentiment for key aspects of products and services. The accuracy of sentiment classification of the product and services has been improved by the optimal selection of weights of neurons in the proposed model. The promising results are obtained by analyzing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed work uses key features such as the positive opinion score, negative opinion score, and term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. The proposed model can be compatible with any sentiment classification problem of products and services.
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Bibliography

  1.  L. Cui, S. Huang, F. Wei, C. Tan, C. Duan, and M. Zhou, “Superagent: A customer service chatbot for E-commerce websites,” in ACL 2017 – 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of System Demonstrations, 2017, pp. 97–102, doi: 10.18653/v1/P17-4017.
  2.  M. Afzaal, M. Usman, and A. Fong, “Tourism mobile app with aspect-based sentiment classification framework for tourist reviews,” IEEE Trans. Consum. Electron. 65(2), 233–242, 2019, doi: 10.1109/TCE.2019.2908944.
  3.  M.S. Akhtar, T. Garg, and A. Ekbal, “Multi-task learning for aspect term extraction and aspect sentiment classification,” Neurocomputing 398, pp. 247–256, 2020, doi: 10.1016/j.neucom.2020.02.093.
  4.  M. Pontiki, D. Galanis, J. Pavlopoulos, H. Papageorgiou, I. Androutsopoulos, and S. Manandhar, “SemEval-2014 Task 4: Aspect Based Sentiment Analysis,” in Proceedings ofthe 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014, pp. 27–35, doi: 10.3115/v1/s14-2004.
  5.  M. Ghiassi, J. Skinner, and D. Zimbra, “Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network,” Expert Syst. Appl. 40(16), 6266–6282, 2013, doi: 10.1016/j.eswa.2013.05.057.
  6.  M. Mladenović, J. Mitrović, C. Krstev, and D. Vitas, “Hybrid sentiment analysis framework for a morphologically rich language,” J. Intell. Inf. Syst. 46(3), 599–620, 2016, doi: 10.1007/s10844-015-0372-5.
  7.  Y. Kai, Y. Cai, H. Dongping, J. Li, Z. Zhou, and X. Lei, “An effective hybrid model for opinion mining and sentiment analysis,” in IEEE International Conference on Big Data and Smart Computing, BigComp 2017, 2017, pp. 465–466, doi: 10.1109/BIGCOMP.2017.7881759.
  8.  F. Iqbal et al., “A Hybrid Framework for Sentiment Analysis Using Genetic Algorithm Based Feature Reduction,” IEEE Access 7, pp. 14637–14652, 2019, doi: 10.1109/ACCESS.2019.2892852.
  9.  J.R. Alharbi and W.S. Alhalabi, “Hybrid approach for sentiment analysis of twitter posts using a dictionary-based approach and fuzzy logic methods: Study case on cloud service providers,” Int. J. Semant. Web Inf. Syst. 16(1), 116–145, 2020, doi: 10.4018/IJSWIS.2020010106.
  10.  S.C. Cagan, M. Aci, B.B. Buldum, and C. Aci, “Artificial neural networks in mechanical surface enhancement technique for the prediction of surface roughness and microhardness of magnesium alloy,” Bull. Polish Acad. Sci. Tech. Sci. 67(4), 729–739, 2019, doi: 10.24425/ bpasts.2019.130182.
  11.  B. Paprocki, A. Pregowska, and J. Szczepanski, “Optimizing information processing in brain-inspired neural networks,” Bull. Polish Acad. Sci. Tech. Sci. 68(2), 225–233, 2020, doi: 10.24425/bpasts.2020.131844.
  12.  I. Rojek and E. Dostatni, “Machine learning methods for optimal compatibility of materials in ecodesign,” Bull. Polish Acad. Sci. Tech. Sci. 68(2), 199–206, 2020, doi: 10.24425/bpasts.2020.131848.
  13.  S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi, “Optimization by simulated annealing,” Science 220(4598), 671–680, 1983, doi: 10.1126/ science.220.4598.671.
  14.  F.F. Moghaddam, R.F. Moghaddam, and M. Cheriet, “Curved Space Optimization: A Random Search based on General Relativity Theory,” pp. 1–16, 2012, [Online]. Available: http://arxiv.org/abs/1208.2214.
  15.  S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Adv. Eng. Softw. 95, pp. 51–67, 2016, doi: 10.1016/j.advengsoft.2016.01.008.
  16.  T. Brychcín, M. Konkol, and J. Steinberger, “UWB: Machine Learning Approach to Aspect-Based Sentiment Analysis,” in Proc. 8th Int. Workshop Semantic Eval. (SemEval) (2014), 2015, no. SemEval, pp. 817–822, doi: 10.3115/v1/s14-2145.
  17.  J. Singh, G. Singh, and R. Singh, “Optimization of sentiment analysis using machine learning classifiers,” Human-centric Comput. Inf. Sci. 7(1), 2017, doi: 10.1186/s13673-017-0116-3.
  18.  M. Al-Smadi, O. Qawasmeh, M. Al-Ayyoub, Y. Jararweh, and B. Gupta, “Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews,” J. Comput. Sci. 27, pp. 386‒393, 2018, doi: 10.1016/j.jocs.2017.11.006.
  19.  P. Kalarani and S. Selva Brunda, “Sentiment analysis by POS and joint sentiment topic features using SVM and ANN,” Soft Comput. 23(16), 7067–7079, 2019, doi: 10.1007/s00500-018-3349-9.
  20.  L. Haghnegahdar and Y. Wang, “A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection,” Neural Comput. Appl. 32(13), 9427–9441, 2020, doi: 10.1007/s00521-019-04453-w.
  21.  J. Zhou, Q. Chen, J.X. Huang, Q. V. Hu, and L. He, “Position-aware hierarchical transfer model for aspect-level sentiment classification,” Inf. Sci. (Ny). 513, pp. 1–16, 2020, doi: 10.1016/j.ins.2019.11.048.
  22.  A.K. J and S. Abirami, “Aspect-based opinion ranking framework for product reviews using a Spearman’s rank correlation coefficient method,” Inf. Sci. (Ny). 460–461, pp. 23–41, 2018, doi: 10.1016/j.ins.2018.05.003.
  23.  C. Cortes and V. Vapnik, “Support-Vector Networks,” Mach. Learn. 20, pp. 273–297, 1995, doi: 10.1109/64.163674.
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Authors and Affiliations

Nallathambi Balaganesh
1
ORCID: ORCID
K. Muneeswaran
1
ORCID: ORCID
  1. Department of Computer Science & Engineering, Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamilnadu, India

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