Dose My Opinion Count? A CNN-LSTM Approach for Sentiment Analysis of Indian General Elections
DOI:
https://doi.org/10.53469/jtpes.2024.04(05).06Keywords:
Sentiment analysis, Reddit, CNN-LSTMAbstract
Sentiment analysis on social media platforms is a critical area of research for understanding public opinion, particularly during significant events like elections. This paper presents a sentiment analysis study using datasets from Reddit. Traditional sentiment analysis methods, while useful, often struggle with the informal and diverse nature of social media language, including sarcasm and contextual nuances. To address these challenges, we explore the use of a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. This CNN-LSTM approach leverages the strengths of both architectures, capturing local features and contextual dependencies in the text. Our study aims to enhance sentiment classification accuracy and demonstrate the efficacy of the CNN-LSTM model in processing large-scale social media datasets.
References
Whitelaw, C., Garg, N., & Argamon, S. (2005, October). Using appraisal groups for sentiment analysis. In Proceedings of the 14th ACM international conference on Information and knowledge management (pp. 625-631).
Fang, X., & Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big data, 2, 1-14.
Balahur, A., Steinberger, R., Kabadjov, M., Zavarella, V., Van Der Goot, E., Halkia, M., ... & Belyaeva, J. (2013). Sentiment analysis in the news. arXiv preprint arXiv:1309.6202.
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal, 5(4), 1093-1113.
Devika, M. D., Sunitha, C., & Ganesh, A. (2016). Sentiment analysis: a comparative study on different approaches. Procedia Computer Science, 87, 44-49.
Taboada, M. (2016). Sentiment analysis: An overview from linguistics. Annual Review of Linguistics, 2, 325-347.
Mohammad, S. M. (2017). Challenges in sentiment analysis. A practical guide to sentiment analysis, 61-83.
Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences, 30(4), 330-338.
Yue, L., Chen, W., Li, X., Zuo, W., & Yin, M. (2019). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60, 617-663.
Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134.
Huang, J., Gu, S. S., Hou, L., Wu, Y., Wang, X., Yu, H., & Han, J. (2022). Large language models can self-improve. arXiv preprint arXiv:2210.11610.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.
Zhou, H., Lou, Y., Xiong, J., Wang, Y., & Liu, Y. (2023). Improvement of Deep Learning Model for Gastrointestinal Tract Segmentation Surgery. Frontiers in Computing and Intelligent Systems, 6(1), 103-106.
Chen, Y. (2015). Convolutional neural network for sentence classification (Master's thesis, University of Waterloo).
Smagulova, K., & James, A. P. (2019). A survey on LSTM memristive neural network architectures and applications. The European Physical Journal Special Topics, 228(10), 2313-2324.
Liu, S., Zhang, C., & Ma, J. (2017). CNN-LSTM neural network model for quantitative strategy analysis in stock markets. In Neural Information Processing: 24th International Conference, ICONIP 2017, Guangzhou, China, November 14-18, 2017, Proceedings, Part II 24 (pp. 198-206). Springer International Publishing.
Lu, W., Li, J., Li, Y., Sun, A., & Wang, J. (2020). A CNN-LSTM-based model to forecast stock prices. Complexity, 2020, 1-10.
Peng, Q., Zheng, C., & Chen, C. (2023). Source-free domain adaptive human pose estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4826-4836).
Popokh, L., Su, J., Nair, S., & Olinick, E. (2021, September). IllumiCore: Optimization Modeling and Implementation for Efficient VNF Placement. In 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) (pp. 1-7). IEEE.
Bao, W., Che, H., & Zhang, J. (2020, December). Will_Go at SemEval-2020 Task 3: An accurate model for predicting the (graded) effect of context in word similarity based on BERT. In Proceedings of the Fourteenth Workshop on Semantic Evaluation (pp. 301-306).
Zhou, Z., Xu, C., Qiao, Y., Ni, F., & Xiong, J. (2024). An Analysis of the Application of Machine Learning in Network Security. Journal of Industrial Engineering and Applied Science, 2(2), 5-12.
Su, J., Nair, S., & Popokh, L. (2023, February). EdgeGYM: a reinforcement learning environment for constraint-aware NFV resource allocation. In 2023 IEEE 2nd International Conference on AI in Cybersecurity (ICAIC) (pp. 1-7). IEEE.
Tan, Z., Cheng, L., Wang, S., Yuan, B., Li, J., & Liu, H. (2024, April). Interpreting pretrained language models via concept bottlenecks. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 56-74). Singapore: Springer Nature Singapore.
Zhou, Z., Xu, C., Qiao, Y., Xiong, J., & Yu, J. (2024). Enhancing Equipment Health Prediction with Enhanced SMOTE-KNN. Journal of Industrial Engineering and Applied Science, 2(2), 13-20.
Luo, Y., Wei, Z., Xu, G., Li, Z., Xie, Y., & Yin, Y. (2024). Enhancing E-commerce Chatbots with Falcon-7B and 16-bit Full Quantization. Journal of Theory and Practice of Engineering Science, 4(02), 52-57.
Ding, R., Zhu, E. Y., Zhao, C., Yang, H., Li, J., & Wu, Y. (2024). Research on Optimizing Lightweight Small Models Based on Generating Training Data with ChatGPT. Journal of Industrial Engineering and Applied Science, 2(2), 39-45.
Xiong, J., Feng, M., Wang, X., Jiang, C., Zhang, N., & Zhao, Z. (2024). Decoding sentiments: Enhancing covid-19 tweet analysis through bert-rcnn fusion. Journal of Theory and Practice of Engineering Science, 4(01), 86-93.
Yin, Y., Xu, G., Xie, Y., Luo, Y., Wei, Z., & Li, Z. (2024). Utilizing Deep Learning for Crystal System Classification in Lithium-Ion Batteries. Journal of Theory and Practice of Engineering Science, 4(03), 199-206.
Liu, H., Shen, Y., Zhou, W., Zou, Y., Zhou, C., & He, S. (2024). Adaptive speed planning for Unmanned Vehicle Based on Deep Reinforcement Learning. arXiv preprint arXiv:2404.17379.
Su, J., Nair, S., & Popokh, L. (2022, November). Optimal resource allocation in sdn/nfv-enabled networks via deep reinforcement learning. In 2022 IEEE Ninth International Conference on Communications and Networking (ComNet) (pp. 1-7). IEEE.
Xie, Y., Li, Z., Yin, Y., Wei, Z., Xu, G., & Luo, Y. (2024). Advancing Legal Citation Text Classification A Conv1D-Based Approach for Multi-Class Classification. Journal of Theory and Practice of Engineering Science, 4(02), 15-22.
Pinyoanuntapong, E., Ali, A., Jakkala, K., Wang, P., Lee, M., Peng, Q., ... & Sun, Z. (2023, September). Gaitsada: Self-aligned domain adaptation for mmwave gait recognition. In 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS) (pp. 218-226). IEEE.
Li, M., He, J., Jiang, G., & Wang, H. (2024). DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM with Joint Semantic Encoding. arXiv preprint arXiv:2401.01545.
Su, G., Wang, J., Xu, X., Wang, Y., & Wang, C. (2024). The Utilization of Homomorphic Encryption Technology Grounded on Artificial Intelligence for Privacy Preservation. International Journal of Computer Science and Information Technology, 2(1), 52-58.
Wang, X., Qiao, Y., Xiong, J., Zhao, Z., Zhang, N., Feng, M., & Jiang, C. (2024). Advanced Network Intrusion Detection with TabTransformer. Journal of Theory and Practice of Engineering Science, 4(03), 191-198.
Liu, H., Shen, Y., Yu, S., Gao, Z., & Wu, T. (2024). Deep reinforcement learning for mobile robot path planning. arXiv preprint arXiv:2404.06974.
Wang, Q., Wang, C., Lai, Z., & Zhou, Y. (2024). Insectmamba: Insect pest classification with state space model. arXiv preprint arXiv:2404.03611.
Peng, Q., Zheng, C., & Chen, C. (2023). Source-free domain adaptive human pose estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4826-4836).
Qiao, Y., Ni, F., Xia, T., Chen, W., & Xiong, J. (2024, January). Automatic recognition of static phenomena in retouched images: A novel approach. In The 1st International scientific and practical conference “Advanced technologies for the implementation of new ideas”(January 09-12, 2024) Brussels, Belgium. International Science Group. 2024. 349 p. (p. 287).
Shen, Y., Liu, H., Liu, X., Zhou, W., Zhou, C., & Chen, Y. (2024). Localization Through Particle Filter Powered Neural Network Estimated Monocular Camera Poses. arXiv preprint arXiv:2404.17685.
Zhao, S., Gan, L., Tuan, L. A., Fu, J., Lyu, L., Jia, M., & Wen, J. (2024). Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning. arXiv preprint arXiv:2402.12168.
Peng, Q., Ding, Z., Lyu, L., Sun, L., & Chen, C. (2022). RAIN: regularization on input and network for black-box domain adaptation. arXiv preprint arXiv:2208.10531.
Xu, C., Qiao, Y., Zhou, Z., Ni, F., & Xiong, J. (2024). Accelerating Semi-Asynchronous Federated Learning. arXiv preprint arXiv:2402.10991.
Lai, Z., Zhang, X., & Chen, S. (2024). Adaptive ensembles of fine-tuned transformers for llm-generated text detection. arXiv preprint arXiv:2403.13335.
Wang, H., Zhou, Y., Perez, E., & Roemer, F. (2024). Jointly Learning Selection Matrices For Transmitters, Receivers And Fourier Coefficients In Multichannel Imaging. arXiv preprint arXiv:2402.19023.
Zhao, Z., Zhang, N., Xiong, J., Feng, M., Jiang, C., & Wang, X. (2024). Enhancing E-commerce Recommendations: Unveiling Insights from Customer Reviews with BERTFusionDNN. Journal of Theory and Practice of Engineering Science, 4(02), 38-44.
Su, Jing, et al. "Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review." arXiv preprint arXiv:2402.10350 (2024).
Tan, Z., Beigi, A., Wang, S., Guo, R., Bhattacharjee, A., Jiang, B., ... & Liu, H. (2024). Large Language Models for Data Annotation: A Survey. arXiv preprint arXiv:2402.13446.
Zhao, S., Jia, M., Tuan, L. A., & Wen, J. (2024). Universal Vulnerabilities in Large Language Models: In-context Learning Backdoor Attacks. arXiv preprint arXiv:2401.05949.
Li, P., Abouelenien, M., & Mihalcea, R. (2023). Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks. arXiv preprint arXiv:2311.10944.
Yan, C., Qiu, Y., & Zhu, Y. (2021). Predict Oil Production with LSTM Neural Network. In Proceedings of the 9th International Conference on Computer Engineering and Networks (pp. 357-364). Springer Singapore.
Feng, M., Wang, X., Zhao, Z., Jiang, C., Xiong, J., & Zhang, N. (2024). Enhanced Heart Attack Prediction Using eXtreme Gradient Boosting. Journal of Theory and Practice of Engineering Science, 4(04), 9-16.
Zhou, Y., Osman, A., Willms, M., Kunz, A., Philipp, S., Blatt, J., & Eul, S. (2023). Semantic Wireframe Detection.
Li, Z., Yin, Y., Wei, Z., Luo, Y., Xu, G., & Xie, Y. (2024). High-Precision Neuronal Segmentation: An Ensemble of YOLOX, Mask R-CNN, and UPerNet. Journal of Theory and Practice of Engineering Science, 4(04), 45-52.
Ru, J., Yu, H., Liu, H., Liu, J., Zhang, X., & Xu, H. (2022). A Bounded Near-Bottom Cruise Trajectory Planning Algorithm for Underwater Vehicles. Journal of Marine Science and Engineering, 11(1), 7.
Zhang, X., Liu, H., Xue, L., Li, X., Guo, W., Yu, S., ... & Xu, H. (2021, September). Multi-objective Collaborative Optimization Algorithm for Heterogeneous Cooperative Tasks Based on Conflict Resolution. In International Conference on Autonomous Unmanned Systems (pp. 2548-2557). Singapore: Springer Singapore.
Lai, Z., Wu, J., Chen, S., Zhou, Y., Hovakimyan, A., & Hovakimyan, N. (2024). Language models are free boosters for biomedical imaging tasks. arXiv preprint arXiv:2403.17343.
Tan, Z., Chen, T., Zhang, Z., & Liu, H. (2024, March). Sparsity-guided holistic explanation for llms with interpretable inference-time intervention. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 19, pp. 21619-21627).
Li, Z., Yu, H., Xu, J., Liu, J., & Mo, Y. (2023). Stock market analysis and prediction using LSTM: A case study on technology stocks. Innovations in Applied Engineering and Technology, 1-6.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Ning Zhang, Jize Xiong, Zhiming Zhao, Mingyang Feng, Xiaosong Wang, Yuxin Qiao, Chufeng Jiang
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.