Aspect Based Sentiment Analysis on a Novel Bangla Dataset Using Transformers

Simple item page
dc.contributor.authorRahman, Mumtahina
dc.contributor.authorFatiha, Kaniz
dc.contributor.authorRahman, Mehesum
dc.date.accessioned2025-03-11T07:26:34Z
dc.date.available2025-03-11T07:26:34Z
dc.date.issued2024-07-05
dc.descriptionSupervised by Md. Nazmul Haque, Assistant Professor, Department of Computer Science and Engineering (CSE) Islamic University of Technology (IUT) Board Bazar, Gazipur, Bangladesh This thesis is submitted in partial fulfillment of the requirement for the degree of Bachelor of Science in Software Engineering, 2024en_US
dc.description.abstractAbstracts are crucial summaries of a thesis, balancing brevity and clarity while ef- fectively conveying the study’s essence. Despite their importance, many writers struggle with creating abstracts that are neither too vague nor overly detailed. This abstract tackles the challenge by offering a structured approach to abstract writing, breaking down the process into five essential components: Introduction, Problem, Proposed Solution, Results, and Conclusion. Using humorous examples, the ab- stract makes the abstract-writing process engaging and accessible. Our method demonstrates significant improvements in the quality and readability of abstracts, as the humor helps demystify and simplify the process. Ultimately, effective ab- stract writing is both an art and a science, requiring precision, audience awareness, and a touch of fun to make it approachable and enjoyable.en_US
dc.identifier.citation[1] M. Abdin, S. A. Jacobs, A. A. Awan, et al., “Phi-3 technical report: A highly capable language model locally on your phone,” arXiv preprint arXiv:2404.14219, 2024. [2] S. Baccianella, A. Esuli, and F. Sebastiani, “Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining,” LREC, 2010. [3] A. Bhattacharjee, T. Hasan, W. U. Ahmad, and R. Shahriyar, “Banglanlg: Benchmarks and resources for evaluating low-resource natural language gen eration in bangla,” CoRR, vol. abs/2205.11081, 2022. arXiv: 2205 . 11081. [Online]. Available: https://arxiv.org/abs/2205.11081. [4] P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, “Enriching word vectors with subword information,” in TACL, 2017. [5] E. Cambria, S. Poria, R. Bajpai, and B. Schuller, “Senticnet 4: A semantic resource for sentiment analysis based on conceptual primitives,” COLING, 2014. [6] D. Chen and C. D. Manning, “A fast and accurate dependency parser using neural networks,” EMNLP, 2014. [7] J. Choi and M. Palmer, “Part-of-speech tagging,” Natural Language Process ing, 2010. [8] M. Hu and B. Liu, “Mining opinion features in customer reviews,” in AAAI, 2004. [9] N. Jakob and I. Gurevych, “Extracting opinion targets in a single and cross domain setting with conditional random fields,” in EMNLP, 2010. [10] Y. Kim, “Convolutional neural networks for sentence classification,” in EMNLP, 2014. [11] B. Liu, “Sentiment analysis and opinion mining,” Synthesis Lectures on Human Language Technologies, 2012. 53 [12] B. Liu and L. Zhang, “Deep learning for aspect-based sentiment analysis: A comparative review,” ACM Transactions on Intelligent Systems and Technol ogy, 2017. [13] Q. Liu, H. Zhang, Z. Lin, J. Xiao, E. Zhou, and H. Xu, “Fine-grained opinion mining with recurrent neural networks and word embeddings,” in EMNLP, 2015. [14] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in ICLR, 2013. [15] M. S. H. Mukta, M. A. Islam, F. A. Khan, et al., “A comprehensive guideline for bengali sentiment annotation,” Transactions on Asian and Low-Resource Language Information Processing, vol. 21, no. 2, pp. 1–19, 2021. [16] R. Narayanan, B. Liu, and A. Choudhary, “Joint phrase extraction and sen timent classification,” in ICDM, 2009. [17] T. H. Nguyen and R. Grishman, “Joint event extraction via recurrent neural networks,” in NAACL, 2015. [18] B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Foundations and Trends in Information Retrieval, 2008. [19] J. Pennington, R. Socher, and C. D. Manning, “Glove: Global vectors for word representation,” in EMNLP, 2014. [20] S. Poria, E. Cambria, R. Bajpai, and A. Hussain, “Aspect extraction using deep learning,” Knowledge-Based Systems, 2016. [21] M. A. Qureshi, M. Asif, M. F. Hassan, et al., “A novel auto-annotation tech nique for aspect level sentiment analysis,” Computers, Materials and Con tinua, vol. 70, no. 3, pp. 4987–5004, 2022. [22] E. Riloff and J. Wiebe, “Learning extraction patterns for subjective expres sions,” EMNLP, 2003. [23] D. Tang, B. Qin, and T. Liu, “Effective lstms for target-dependent sentiment classification,” in COLING, 2016. [24] D. Tang, F. Wei, B. Qin, T. Liu, and M. Zhou, “Learning sentiment-specific word embedding for twitter sentiment classification,” ACL, 2015. [25] D. Tian, K. He, and H. Yan, “Maxent classifiers for named entity recognition of chinese financial texts,” in EMNLP, 2015. 54 [26] D. S. Vargas, L. R. Pessutto, and V. P. Moreira, “Unsupervised aspect term extraction for sentiment analysis through automatic labeling.,” in WEBIST, 2022, pp. 344–354. [27] H. Wang, D. Can, A. Kazemzadeh, F. Bar, and S. Narayanan, “A system for real-time twitter sentiment analysis of 2012 us presidential election cycle,” ACL, 2011. [28] Y. Wang, M. Huang, L. Zhu, and X. Zhao, “Attention-based lstm for aspect level sentiment classification,” in EMNLP, 2016. [29] H. Xu, B. Liu, L. Shu, and P. S. Yu, “Position-aware attention and contextual triple embeddings for aspect sentiment classification,” in IJCAI, 2018. [30] H. Yang, C. Zhang, and K. Li, “Pyabsa: A modularized framework for re producible aspect-based sentiment analysis,” in Proceedings of the 32nd ACM international conference on information and knowledge management, 2023, pp. 5117–5122. [31] M. Zhang, Y. Zhang, and D.-T. Vo, “Enhancing aspect-based sentiment anal ysis with context aware aspect embeddings,” in EMNLP, 2019. [32] X. Zhang, J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” in NIPS, 2015.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/2384
dc.language.isoenen_US
dc.publisherDepartment of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladeshen_US
dc.subjectABSA, Sentiment analysis, LLM, Bengali Dataset, NLPen_US
dc.titleAspect Based Sentiment Analysis on a Novel Bangla Dataset Using Transformersen_US
dc.typeThesisen_US

Files

Original bundle

Now showing 1 - 3 of 3
Thumbnail Image
Name:
Fulltext_inbound7110465860797833791 - Mumtahina Rahman, 190042129.pdf
Size:
1.63 MB
Format:
Adobe Portable Document Format
Description:
Download
Thumbnail Image
Name:
Signature Page_ inbound1709688048353954882 - Mumtahina Rahman, 190042129 (1).pdf
Size:
244.85 KB
Format:
Adobe Portable Document Format
Description:
Download
Thumbnail Image
Name:
Turnitin Report_ inbound1596957838604873251 - Mumtahina Rahman, 190042129 (1).pdf
Size:
170.36 KB
Format:
Adobe Portable Document Format
Description:
Download

License bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

2024