MLOps Automation – Developing a RESTful API for Text Based Emotion Detection
Pages 666 - 672
Abstract
Identifying the emotional state of a person or group of people by analyzing their written works often appears to be challenging but also important. In many situations, we see that emotions from textual expressions cannot be detected directly using emotional words alone but also results from the evaluation of the conceptual meaning and interactions which are expressed in a written article. A person's emotion may be revealed by their facial expression, speech, or text. In todays’ world we have seen that efforts have been made in identifying emotion from speech and facial expressions, but the aspect of text-based emotion detection is still lagging. The detection of human emotions from text is becoming progressively important from an applicative perspective. The paper proposed an algorithm for detecting emotions from text using logistic regression. The authors have developed a RESTful API to serve as a mediator between a client device and the trained model by adopting the principles of Machine Learning Operations (MLOps).
References
[1]
W. G. Parrott, in Emotions in Social Psychology: Key Readings in Social Psychology, Psychology Press, 2001, pp. 156-265.
[2]
e. a. Ang j, “Prosody-Based Automatic Detection Of Annoyance And Frustration In Human-Computer Dialog,” in Icslp, 2002.
[3]
J. S. e. al, “Algorithms for the Control of Key Performance,” in Procedia Computer Science, 2020.
[4]
D. A. Tamburri, “Sustainable MLOps: Trends and Challenges,” in 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2020.
[5]
P. Lipton, D. Palma, M. Rutkowski and D. A. Tamburri, “Tosca solves big problems in the cloud and beyond!,” IEEE Cloud Computing, vol. 5, no. 2, pp. 37-47, 2018.
[6]
S. Azmin and K. Dhar, “Emotion Detection from Bangla Text Corpus Using Naïve Bayes Classifier,” in 4th International Conference on Electrical Information and Communication Technology (EICT), 2019.
[7]
M. Li, H. Xu, X. Liu and S. Lu, “Emotion recognition from multichannel EEG signals using K-nearest neighbor classification,” in Technology and Health Care 26, 2018.
[8]
J. Ranganathan, N. Hedge, A. S. Irudayaraj and A. A. Tzacheva, “Automatic Detection of Emotions in Twitter Data - A Scalable Decision Tree Classification Method,” in In Proceedings of ACM conference on Hypertext and Social Media (RevOpID ’2018)., New Yor, 2018.
[9]
V. Gajarla and A. Gupta, “Emotion Detection and Sentiment Analysis of Images,” Georgia Institute of Technology.
[10]
M. Masse, REST API Design Rulebook: Designing Consistent RESTful, O'Reilly Media, Inc, 2011.
[11]
M. Biehi, “api-university.com,” 03 March 2020. [Online]. Available: https://api-university.com/blog/architectural-style-for-apis/. [Accessed 8 January 2022].
[12]
N. Evi, S. Garth, H. T. R., W. Ben and M. Dan, in UNIX and Linux System Administration Handbook, Addison-Wesley Professional, 2017, pp. 692-694.
[13]
R. B. J. E. Joshua Ofoeda, “Application Programming Interface (API) Research,” International Journal of Enterprise Information Systems, vol. 15, no. 3, pp. 77-80, 2019.
[14]
R. T. Fielding, “Architectural Styles and the Design of Network-based,” in PhD thesis,University of California, Irvine, 2000.
[15]
D. E. Drummond, “Open sourcing education for Data Engineering and Data Science,” in IEEE Frontiers in Education Conference (FIE), 2016.
[16]
M. Sasu, S. Henrik, L. Eero and M. Tommi, “Who Needs MLOps: What Data Scientists Seek to,” in IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN), 2021.
[17]
A. Pant, “Introduction to Logistic Regression,” 22 January 2019. [Online]. Available: https://towardsdatascience.com/introduction-to-logisticregression-66248243c148. [Accessed 02 February 2022].
[18]
J. Browniee, “Logistic Regression for Machine Learning,” 1 April 2016. [Online]. Available: https://machinelearningmastery.com/logisticregression-for-machine-learning/. [Accessed 5 January 2022].
[19]
J. Charis, "GitHub," [Online]. Available: https://github.com/Jcharis/end2end-nlp-project/blob/main/data/emotion_dataset_2.csv. [Accessed 06 December 2021].
[20]
L. Mary and D. G, “Mary, L., & G, D. (2018). Keyword Spotting Techniques. Searching Speech Databases, 45–60.,” 2018.
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Published In
August 2022
710 pages
ISBN:9781450396752
DOI:10.1145/3549206
- General Chairs:
- Sartaj Sahni,
- Vikas Saxena,
- Program Chair:
- Sundaraja Sitharama Iyengar
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Publication History
Published: 24 October 2022
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IC3-2022
IC3-2022: 2022 Fourteenth International Conference on Contemporary Computing
August 4 - 6, 2022
Noida, India
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