AI Model for Sentiment Analysis System Using Python

References

[1] Khan, Rijwan, et al. "Social media analysis with AI: sentiment analysis techniques for the analysis of Twitter covid-19 data." J. Crit. Rev 7.9 (2020).

[2] Samal, BiswaRanjan, Anil Kumar Behera, and Mrutyunjaya Panda. "Performance analysis of supervised machine learning techniques for sentiment analysis." 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS). IEEE, 2017.

[3] Hemalatha, S., and Ramathmika Ramathmika. "Sentiment analysis of Yelp reviews by machine learning." 2019 International Conference on Intelligent Computing and Control Systems (ICCS). IEEE, 2019.

[4] Hota, Soudamini, and Sudhir Pathak. "KNN classifier-based approach for multi-class sentiment analysis of Twitter data." Int. J. Eng. Technol 7.3 (2018): 1372-1375.

[5] T. Carpenter, and T. Way, “Tracking Sentiment Analysis through Twitter,”. ACM computer survey. Villanova: Villanova University, 2010.

[6] Saif, H., He, Y., Alani, H. (2012). Semantic Sentiment Analysis of Twitter. In: Cudré- Mauroux, P., et al. The Semantic Web – ISWC 2012. ISWC 2012.

[7] Sarlan, Aliza & Nadam, Chayanit & Basri, Shuib. (2014). Twitter sentiment analysis. 212-216.

[8] Kharde, Vishal & Sonawane, Sheetal. (2016). Sentiment Analysis of Twitter Data: A Survey of Techniques. International Journal of Computer Applications. 139. 5-15.

[9] M. Hagen, M. Potthast, M. Büchner, and B. Stein, "Webis: An ensemble for Twitter sentiment detection," in Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015), 2015, pp. 582-589.

[10] Sarkar, Dipanjan. Text analytics with Python: a practitioner's guide to natural language processing. Bangalore: Apress, 2019.

[11] Neelakandan, S., and D. Paulraj. "An automated learning model of conventional neural network-based sentiment analysis on Twitter data." Journal of Computational and Theoretical Nanoscience 17.5 (2020)

[12] Razno, Maria. "Machine learning text classification model with NLP approach." Com- putational Linguistics and Intelligent Systems 2 (2019): 71-73.

@article{1705456,
      author = {Kiran Pal, Dr. Santosh Singh, Rimsy Dua, Lisa Rodrigues},
      title = {AI Model for Sentiment Analysis System Using Python},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {8},
      pages = {41-45},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705456.pdf},
      abstract = {Sentiment analysis is an important task in natural language processing that aims to understand and classify opinions and emotions represented in text data. This research article introduces a complete way to develop an AI model for sentiment analysis using the Python programming language. The study makes use of a labeled Twitter dataset, which includes messages with positive, negative, and neutral attitudes. On this dataset, machine learning model is trained and assessed, with a focus on preprocessing stages such as text cleaning, tokenization, and feature extraction. The suggested system makes use of cutting-edge machine learning and deep learning algorithms, as well as Natural Language Processing (NLP) libraries. Data pre-processing, feature extraction, and the use of various text representation approaches, such as word embeddings, are all part of the model construction process.},
      keywords = {Random Forest, Health, Text, Machine Learning, Sentiment, Voice, Twitter},
      month = {February},
  }