A Research Paper on Negation Handling: Sentiment Analysis Using Super Ensemble Method in Deep Learning
DOI:
https://doi.org/10.17010/ijcs/2023/v8/i3/172862Keywords:
Sentiment Analysis
, Negation Handling, Super Ensemble Method, Two Level Ensemble.Paper Submission Date
, March 12, 2023, Paper sent back for Revision, March 20, Paper Acceptance Date, March 23, Paper Published Online, June 5, 2023Abstract
Sentiment analysis, a vital technique for comprehending the ideas and attitudes represented in natural language text includes negation handling as a key component. The technique of automatically identifying and categorising the polarity of the sentiment expressed in a text, which might be positive, negative, or neutral, is known as sentiment analysis. Negation handling is tricky too because words like "not", "no", and "never" can flip the polarity of a mood, making it challenging to recognise the sentiment being represented. Therefore, in this research paper, a model is proposed using the super ensemble technique which can correctly comprehend the sentiments expressed in the reviews and give approximately 96% accuracy.Downloads
Downloads
Published
How to Cite
Issue
Section
References
A. Al-Thubaity, Q. Alqahtani, and A. Aljandal, “Sentiment lexicon for sentiment analysis of Saudi dialect tweets,†Procedia Comp. Sci., vol. 142, pp. 301–307, Nov. 2018, doi: 10.1016/j.procs.2018.10.494.
A. Abdi, S. M. Shamsuddin, S. Hasan, and J. Piran, “Machine learning-based multi-documents sentiment-oriented summarization using linguistic treatment,†Expert Syst. Appl., vol. 109, pp. 66–85, Nov. 2018, doi: 10.1016/j.eswa.2018.05.010.
C. Shang, M. Li, S. Feng, Q. Jiang, and J. Fan, “Feature selection via maximizing global information gain for text classification,†Knowl. Based Syst., vol. 54, pp. 298–309, Dec. 2013, doi: 10.1016/j.knosys.2013.09.019.
H. Xu, B. Liu, L. Shu, and P. Yu, “Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction.†Accessed: Dec. 06, 2022. [Online]. Available: https://arxiv.org/pdf/1805.04601.pdf
Y. Wang, M. Huang, L. Zhao, and X. Zhu, “Attention-based LSTM for aspect-level sentiment classification,†in Proc. 2016 Conf. Empirical Methods Natural Lang. Process., pp. 606–615, Austin, Texas, November 1–5, 2016. [Online]. Available: https://aclanthology.org/D16-1058.pdf
Y. Xing and C. Xiao, “A GRU Model for Aspect Level Sentiment Analysis,†J. Physics: Conf. Series, vol. 1302, p. 3, 032042, Aug. 2019, doi: 10.1088/1742-6596/1302/3/032042.
D. Gautam, N. Maharjan, R. Banjade, L. J. Tamang, and V. Rus. "Long Short Term Memory Based Models for negation handling in tutorial dialogues." in FLAIRS Conf., pp. 14–19. 2018, doi: 10.13140/RG.2.2.26250.36804.
W. Liu, M. Zhang, Z. Luo, and Y. Cai, “An Ensemble Deep Learning Method for vehicle type classification on Visual Traffic Surveillance sensors,†IEEE Access, vol. 5, pp. 24417–24425, 2017, doi: 10.1109/ACCESS.2017.2766203.
F. Yang, C. Du, and L. Huang, “Ensemble sentiment analysis method based on R-CNN and C-RNN with Fusion Gate,†Int. J. Comput. Commun. Control, vol. 14, no. 2, pp. 272–285, Apr. 2019. [Online]. Available: https://univagora.ro/jour/index.php/ijccc/article/view/3375. Accessed: May 9, 2023.
D. Nozza, E. Fersini, and E. Messina, “Deep Learning and Ensemble Methods for Domain Adaptation,†IEEE Xplore, Nov. 1, 2016 in 2016 IEEE 28th Int. Conf. Tools Artif. Intell., San Jose, CA, USA, 2016, pp. 184–189, Accessed: May 9, 2023. doi: 10.1109/ICTAI.2016.0037.
A. Kumar, J. Kim, D. Lyndon, M. Fulham, and D. Feng, “An ensemble of fine-tuned Convolutional Neural Networks for Medical Image Classification,†IEEE J. Biomed. Health Inform., vol. 21, no. 1, pp. 31–40, Jan. 2017, doi: 10.1109/jbhi.2016.2635663.
A. Krogh and J. Vedelsby, “Neural Network Ensembles, Cross Validation, and Active Learning,†Neural Inf. Process. Syst., pp. 231–238, 1994. Accessed: May 9, 2023. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/1994/file/b8c37e33defde51cf91e1e03e51657da-Paper.pdf
Z.-H Zhou. “Ensemble Learning.†In: Li, S.Z., Jain, A. (eds) Encyclopedia Biometrics. Springer, Boston, MA, 2009. doi: 10.1007/978-0-387-73003-5_293.
K. Smagulova, A. P. James, “A survey on LSTM memristive neural network architectures and applications,†Eur. Phys. J. Spec. Top. vol. 228, pp. 2313–2324, 2019, doi: 10.1140/epjst/e2019-900046-x.
S. B. Garg and V. V. Subrahmanyam, “Sentiment analysis: Choosing the Right Word Embedding for Deep Learning Model,†Adv. Comput. Intell.Technologies, pp. 417–428, Jul. 2021, doi: 10.1007/978-981-16-2164-2_33.
S. B. Garg and V. V. Subrahmanyam, “Sentiment analysis: Choosing the right word embedding for deep learning model,†in M. Bianchini, V. Piuri, S. Das, R. N. Shaw (eds) Adv. Comput. Intell. Technologies. Lecture Notes in Networks and Systems, vol. 218, 2022. Singapore : Springer, doi: 10.1007/978-981-16-2164-2_33.
