Abstract
Sentiment analysis is a computational method that extracts emotional keywords from different texts through initial emotion analysis (e.g., Happy, Sad, Positive, Negative & Neutral). A recent study by a human rights organization found that 30% of children in Bangladesh are being abused on online in the COVID-19 epidemic by various obscene comments. The main goal of our research is to collect textual data from social media and classify the way children are harassed by various abusive comments online through the use of emoji in a text-mining method and to expose to society the risks that children face online. Another goal of this study is to set a precedent through a detailed study of child abuse and neglect in the big data age. To make the work effective, 3373 child abusive comments are collected manually from online (e.g. Facebook, Newspapers and various Blogs). At present, there is still a very limited number of Bengali child sentiment analysis studies. Fine-tuned general purpose language representation models, such as the BERT family model (BERT, Distil-BERT), and glove word embedding based CNN and Fast-Text models have been used to successfully complete the study. We show that Distil-BERT defeated BERT, Fast-Text, and CNN by 96.09% (relative) accuracy, while Bert, Fast-Text and CNN have 93.66%, 95.73%, and 95.05%, respectively. But observations show that the accuracy of the Distil-BERT does not differ much from the rest of the models. From our analysis, it can be said that the pre-trained models performed outstanding and in addition, child sentiment analysis can serve as a potential motivator for the government to formulate child protection policies and build child welfare systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Saad, S., Saberi, B.: Sentiment analysis or opinion mining: a review. Int. J. Adv. Sci. Eng. Inf. Technol. 7(5), 1660 (2017)
Varghese, R.: A survey on sentiment analysis and opinion mining. Int. J. Res. Eng. Technol. (2013)
Vinodhini, G., Chandrasekaran, R.M.: Sentiment analysis and opinion mining: a survey. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2, 282–292 (2012)
Pasupa, K., Netisopakul, P., Lertsuksakda, R.: Sentiment analysis of Thai Children’s stories. Artif. Life Robot. 21(3), 357–364 (2016)
Li, Z., Kawamoto, J., Feng, Y., Sakurai, K.: Cyberbullying detection using parent-child relationship between comments. In: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services (2016)
Hankamer, D., Liedtka, D.: Twitter sentiment analysis with emojis (2019)
Yo, B., Rayz, J.: Understanding emojis for sentiment analysis. In: The International FLAIRS Conference Proceedings, vol. 34 (2021)
Novak, P. K., Smailovic, J., Sluban, B., Mozetic, I.: Sentiment of emojis. PLoS ONE 10(12), e0144296 (2015)
Tang, D., Qin, B., Liu, T.: Deep learning for sentiment analysis: Successful approaches and future challenges. Wiley Int. Rev. Data Min. Knowl. Discov. 5(6), 292–303 (2015)
Gautam, G., Yadav, D.: Sentiment analysis of Twitter data using machine learning approaches and semantic analysis. In: Seventh International Conference on Contemporary Computing (IC3), India (2014)
Severyn, A., Moshiti, A.: Twitter sentiment analysis with deep convolutional neural networks. In: The 38th International ACM SIGIR Conference (2015)
Boiy, E., Moens, M.: A machine learning approach to sentiment analysis in multilingual web texts. Inf. Retr. J. 12(5), 526–558 (2009)
Rabeya, T., Ferdous, S., Suhita, H., Chakraborty, N.R.: A survey on emotion detection: a lexicon based backtracking approach for detecting emotion from Bengali text. In: 10th International Conference of Computer and Information Technology (ICCIT) (2017)
Hasan, A., Moin, S., Karim, A., Band, S.S.: Machine learning-based sentimental analysis for Twitter accounts. Math. Comput. Appl. 23(1), 11 (2018)
Azharul Hasan, K.M., Islam, Md.S., Mashrur-E-Elahi, G.M, Izhar, M.N.: Sentiment recognition from Bangla text. In: Technical Challenges and Design Issues in Bangla Language Processing (2013)
Islam, T., Ahmed, N., Latif, S.: An evolutionary approach to comparative analysis of detecting Bangla abusive text. Bull. Electr. Eng. Inform. 10(4), 2163–2169 (2021). International Conference on Innovation in Engineering and Technology (ICIET)
Boonchuay, K.: Sentiment classification using text embedding for Thai teaching evaluation. Appl. Mech. Mater. 886, 221–226 (2019)
Tang, R., Lu, Y., Liu, L., Mou, L., Vechtomova, O., Lin, J.: Distilling task-specific knowledge from BERT into simple neural networks. In: Computation and Language (cs.CL); Machine Learning (cs.LG) (2019)
Li, X., Bing, L., Zhang, W., Lam, W.: Exploring BERT for end-to-end aspect-based sentiment analysis. In: Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019) (2019)
Munikar, M., Shakya, S., Shrestha, A.: Fine-grained sentiment classification using BERT. In: 2019 Artificial Intelligence for Transforming Business and Society (AITB), Kathmandu, Nepal, vol. 1 (2019)
Cach Dang, N., Moreno-Gracia, M.N., De la Prieta, F.: Sentiment analysis based on deep learning: a comparative study. Electronics 9(3), 483 (2020)
Costa-jussa, M.R., Gonzalez, E., Moreno, A., Cumalat, E.: Abusive language in Spanish children and young teenager’s conversations: data preparation and short text classification with contextual word embeddings. In: Proceedings of the 12th Language Resources and Evaluation Conference 2020 (2020)
Malik, P., Aggrawal, A., Vishwakarma, D.K.: Toxic speech detection using traditional machine learning models and BERT and fastText embedding with deep neural networks. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) (2021)
Tran, T., Nguyen, D., Nguyen, A., Golen, E.: Sentiment analysis of marijuana content via Facebook emoji-based reactions. In: 2018 IEEE International Conference on Communications (ICC), pp. 793–798 (2018)
LeCompte, T., Chen, J.: Sentiment analysis of tweets including emoji data. In: 2017 International Conference on Computational Science and Computational Intelligence (CSCI), USA (2017)
Shiha, M.O., Ayvaz, S.: The effects of emoji in sentiment analysis. Int. J. Comput. Electr. Eng. 9, 360–369 (2017)
Wijeratne, S., Balasuriya, L., Sheth, A., Doran, D.: A semantics-based measure of emoji similarity. In: 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Germany (2017)
Kimura, M., Katsurai, M.: Automatic construction of an emoji sentiment lexicon. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2017)
Al-Azani, S., El-Alfy, E.-S.M.: Combining emojis with Arabic textual features for sentiment classification. In: 2018 9th International Conference on Information and Communication Systems (ICICS) (2018)
Tomhira, T., Otsuka, A., Yamashita, A., Satoh, T.: What does your tweet emotion mean? Neural emoji prediction for sentiment analysis. In: Proceedings of the 20th International Conference on Information Integration and Web-Based Applications & Services, pp. 289–269 (2018)
Chen, Y., Yuan, J., You, Q., Luo, J.: Twitter sentiment analysis via bi-sense emoji embedding and attention-based LSTM. In: Proceedings of the 26th ACM International Conference on Multimedia (2018)
Zheng, J., Wang, J., Ren, Y., Yang, Z.: Chinese sentiment analysis of online education and internet buzzwords based on BERT. J. Phys Conf. Ser. 1631, 012034 (2020)
Kottursamy, K.: A review on finding efficient approach to detect customer emotion analysis using deep learning analysis. J. Trends Comput. Sci. Smart Technol. 3(2), 95–113 (2021)
Pandian, A.P.: Performance evaluation and comparison using deep learning techniques in sentiment analysis. J. Soft Comput. Paradigm (JSCP) 3(02), 123–134 (2021)
Ganesan, T., Anuradha, S., Harika, A., Nikitha, N., Nalajala, S.: Analyzing social media data for better understanding students’ learning experiences. In: Hemanth, J., Bestak, R., Chen, J.I.-Z. (eds.) Intelligent Data Communication Technologies and Internet of Things. LNDECT, vol. 57, pp. 523–533. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9509-7_43
Tripathi, M.: Sentiment analysis of Nepali COVID19 tweets using NB, SVM AND LSTM. J. Artif. Intell. 3(03), 151–168 (2021)
Sungheetha, A., Sharma, R.: Transcapsule model for sentiment classification. J. Artif. Intell. 2(03), 163–169 (2020)
Ghosh, M., Gupta, K., Susan, S.: Aspect-based unsupervised negative sentiment analysis. In: Hemanth, J., Bestak, R., Chen, J.I.-Z. (eds.) Intelligent Data Communication Technologies and Internet of Things. Lecture Notes on Data Engineering and Communications Technologies, vol. 57, pp. 335–344. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9509-7_29
Boukabous, M., Azizi, M.: A comparative study of deep learning-based language representation learning models. Indones. J. Electr. Eng. Comput. Sci. 22(2), 1032 (2021)
Tomihira, T., Otsuka, A., Yamashita, A., Satoh, T.: Multilingual emoji prediction using BERT for sentiment analysis. Int. J. Web Inf. Syst. 16(3), 265–280 (2020)
Emon, E.A, Rahman, S., Banarjee, J., Das, A.K., Mittra, T.: A deep learning approach to detect abusive Bengali text. In: 2019 7th International Conference on Smart Computing & Communications (ICSCC), Malaysia, pp. 1–5 (2019)
Lucky, E.A.E., Sany, M.M.H., Keya, M., Khushbu, S.A. Noori, S.R.H.: An attention on sentiment analysis of child abusive public comments towards Bangla text and ML. In: 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1–6 (2021). https://doi.org/10.1109/ICCCNT51525.2021.9580154
Mahmud, M.S., Jaman Bonny, A., Saha, U., Jahan, M., Tuna, Z.F., Al Marouf, A.: Sentiment analysis from user-generated reviews of ride-sharing mobile applications. In: 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), pp. 738–744 (2022). https://doi.org/10.1109/ICCMC53470.2022.9753947
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Saha, U. et al. (2022). Exploring Public Attitude Towards Children by Leveraging Emoji to Track Out Sentiment Using Distil-BERT a Fine-Tuned Model. In: Chen, J.IZ., Tavares, J.M.R.S., Shi, F. (eds) Third International Conference on Image Processing and Capsule Networks. ICIPCN 2022. Lecture Notes in Networks and Systems, vol 514. Springer, Cham. https://doi.org/10.1007/978-3-031-12413-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-12413-6_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-12412-9
Online ISBN: 978-3-031-12413-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)