Studies On Development of Model for Sentiment Analysis with ML: A Review

References

[1] A. Yenkikar, N. Babu and S. Sangve, “R-SA: A Rule-based Expert System for Sentiment Analysis”, 2019 IEEE Pune Section International Conference Pune, India, 2019, pp. 1-7, 10.1109/PuneCon46936.2019.9105682.

[2] S. Islam, X. Dong, “Domain-specific Sentiment Lexicons Induced from Labeled Document”, Proceedings of the 28th International Conference on Computational Linguistics, pages 6576–6587, Barcelona, Spain (Online), 2020. available at: https://aclanthology.org/2020.coling- main.578.pdf.

[3] C. Dang, “Hybrid Deep Learning Models for Sentiment Analysis”, Hindawi, Vo. 2021, https://

[4] L. Zhu, M. Xu, Y. Bao, Y. Xu, X. Kong, “Deep learning for aspect-based sentiment analysis: a review”, PeerJ Computer Science, 10.7717/peerjcs. 1044, 2022.

[5] D. Tang, F. Wei, B. Qin, N. Yang, T. Liu, M. Zhou, “Sentiment Embeddings with Applications to Sentiment Analysis”, IEEE Transactions on Knowledge and Data Engineering, Vol. 28, No. 2, Feb 2016.

[6] L. Yu, K. Li, X. Zhang, “Refining Word Embeddings Using Intensity Scores for Sentiment Analysis”, in IEEE Transactions on Audio, Speech, and Language Processing, Vol. 26, No. 3, March 2018.

[7] R. Zhao and K. Mao, “Topic-Aware Deep Compositional Models for Sentence Classification”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 25, no. 2, Feb. 2017.

[8] A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, “Improving language understanding by generative pre-training”, 2018.

[9] M.Joshi, D. Chen, Y. Liu, D.. Weld, L. Zettlemoyer, O. Levy, “Spanbert: improving pre- training by representing and predicting spans”, arXiv preprint,2020, arXiv:1907.10529

[10] Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. Salakhutdinov, Q. Le, “XLNet: Generalized Autoregressive Pretraining for Language Understanding”, arXiv preprint, 2020, https://arxiv.org/abs/1906.08237

[11] Nandipati Sai Akash, Naveen Sai Bommina, Uppu Lokesh, Hussain Syed, Syed Umar, "Optimized Block Chain-Enabled Security Mechanism for IoT Using Ant Colony Optimization", International Journal on Recent and Innovation Trends in Computing and Communication, (2023), 11(10), 1226–1233.

[12] D. Yan, B. Hu and J. Qin, “Sentiment Analysis for Microblog Related to Finance Based on Rules and Classification”, 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 119-126, 2018.

[13] Mohammed Kasri, Anas El-Ansari, Mohamed El Fissaoui, Badreddine Cherkaoui, Marouane Birjali, Abderrahim Beni-Hssane, “Public sentiment toward renewable energy in Morocco: opinion mining using a rule-based approach”, Social Network Analysis and Mining, Volume 13, Issue 1, 2023.

[14] Kousik Barik and Sanjay Misra, “Analysis of customer reviews with an improved VADER lexicon classifier”, Journal of Big Data, Volume 11, Issue 1, 2024.

[15] Naveen Sai Bommina, Nandipati Sai Akash, Uppu Lokesh, Dr. Hussain Syed, Dr. Syed Umar, "A Hybrid Optimization Framework for Enhancing IoT Security via AI-based Anomaly Detection", International Journal on Recent and Innovation Trends in Computing and Communication, (2023) ISSN: 2321-8169 Volume: 11 Issue: 3.

[16] Wei Lin and Li-Chuan Liao, “Lexicon-based prompt for financial dimensional sentiment analysis”, Expert Systems with Applications, Volume 244, 2024.

[17] B. Pang and L. Lee, “A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts”, in Proc. 42nd Annu. Meeting Assoc. Comput. Linguistics, pp. 115–124., 2004.

[18] Jacqueline Kazmaier, Jan H. van Vuuren, “The power of ensemble learning in sentiment analysis”, Expert Systems with Applications, Volume 187, 2022.

[19] Naveen Sai Bommina , Nandipati Sai Akash, Uppu Lokesh , Dr. Hussain Syed , Dr. Syed Umar, "Privacy-Preserving Federated Learning for IoT Devices with Secure Model Optimization", International Journal of Communication Networks and Information Security (IJCNIS), (2021), 13(2), 396–405.

[20] Ankit and Nabizath Saleena, “An Ensemble Classification System for Twitter Sentiment Analysis”, Procedia Computer Science, vol. 132, pg. 937- 946, 2018.

[21] P. Liu, X. Qiu, X. Huang, “Recurrent Neural Network for Text Classification with Multi-Task Learning”, Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16), pg.2873-2879, 2016.

[22] Uppu Lokesh, Naveen Sai Bommina, Nandipati Sai Akash, Dr. Hussain Syed, Dr. Syed Umar, "Designing Energy-Efficient and Secure IoT Architectures Using Evolutionary Optimization Algorithms", International Journal of Applied Engineering & Technology, Vol. 4 No.2, September, 2022.

[23] D. Tang, B. Qin, F. Wei, L. Dong, T. Liu, M. Zhou, “A Joint Segmentation and Classification Framework for Sentence Level Sentiment Classification”, IEEE/ACM Transaction on Audio, Speech, and Language Processing, Vol. 23, no. 11, Nov 2015.

[24] D. Yin, T. Meng, K. Chang, “SentiBERT: A Transferable Transformer- Based Architecture for Compositional Sentiment Semantics”, arXivpreprint, 2020, arXiv:2005.04114v4

[25] Naveen Sai Bommina , Nandipati Sai Akash, Uppu Lokesh , Dr. Hussain Syed , Dr. Syed Umar, "Multi-Objective Genetic Algorithms for Secure Routing and Data Privacy in IoT Networks", International Journal of Communication Networks and Information Security (IJCNIS), (2020), 12(3), 632–643.

[26] Mr. Dikshendra Daulat Sarpate, and Dr. B.G Nagaraja, "CONVOLUTION NEURAL NETWORK-BASED SPEECH EMOTION RECOGNITION USING MFCCS", International Journal of Communication Networks and Information Security, 2023/12/10

@article{1712456,
      author = {Nagavelli Yogender Nath, Gattu Ramya, R. Prasanth Reddy, K. Mani Raju},
      title = {Studies On Development of Model for Sentiment Analysis with ML: A Review},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {6},
      pages = {1155-1163},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712456.pdf},
      abstract = {Sentiment analysis refers to the automated process of identifying the underlying emotional tone conveyed in a text. This task has become increasingly crucial today due to the exponential growth of opinion-oriented text generation on various online platforms including social media and e-commerce websites. The process of sentiment analysis has evolved through different phases like the use of handcrafted rules, sentiment-oriented dictionaries, machine learning, deep learning, transformer models, and large language models. These different phases have enriched the field of sentiment analysis and enabled it to get better insights into text for sentiment analysis. One of the most impactful developments in sentiment analysis was the creation of embedding?s for words or sentences, which are useful for representing unstructured data as structured data. These embedding are representing words or sentences as numerical vectors in a high-dimensional space and have proven effective in capturing the semantic relationships between words. By leveraging the power of embedding?s, sentiment analysis models can understand the complexities of human language and provide more precise insights into people's emotions and opinions. However, word embedding?s not inherently generated for sentiment analysis, which means that they are not inherently sentiment-oriented. Therefore, many researchers have focused on developing sentiment-oriented word embedding?s for sentiment words, but they have overlooked the importance of intensity words such as ?little?, ?high?, and ?extremely?. These intensity words determine the level of sentiment expressed in text, which is particularly useful for fine-level sentiment analysis. To address this issue, a review proposes an intensity-aware word embedding development model and sentiment analysis with ML.},
      keywords = {Sentiment analysis, Embedding, Machine learning methods, Hybrid methods},
      month = {December},
      doi = {https://doi.org/10.64388/IREV8I6-1712456}
  }