Emotion Recognition Of Animals Using Natural Language Processing

Main Article Content

M. S. Antony Vigil
Amit Kumar Anand
Jainendra Pratap Singh
Swarup Kumar Misra

Abstract

Sentiment analysis, also known as opinion mining, is a Natural Language Processing (NLP) technique that holds a pivotal role in discerning textual data's sentiments, categorizing them as positive, negative, or neutral. Its significance is underscored by its widespread use in aiding businesses to gauge brand and product sentiment from customer feedback, enhancing customer service, and identifying areas for product and service improvement. Moreover, sentiment analysis offers the ability to track sentiments in real-time, helping companies retain existing customers and attract new ones cost-effectively. Emotion recognition in animals using Natural Language Processing (NLP) is a challenging and less explored area compared to human emotion recognition. While animals do communicate their emotions through various non-verbal cues, such as body language, vocalizations, and facial expressions, applying NLP techniques directly may not be straightforward since animals don't use language in the same way humans do. However, if there are textual data associated with animal behavior, such as ethological observations or written descriptions of their activities, NLP techniques can be adapted to gain insights into their emotional states.


 

Downloads

Download data is not yet available.

Article Details

How to Cite
M. S. Antony Vigil, Amit Kumar Anand, Jainendra Pratap Singh, & Swarup Kumar Misra. (2024). Emotion Recognition Of Animals Using Natural Language Processing. Journal of Advanced Zoology, 45(1), 1233–1242. https://doi.org/10.53555/jaz.v45i1.3692
Section
Articles
Author Biographies

M. S. Antony Vigil

Department of Computer Science and Engineering

Amit Kumar Anand

Department of Computer Science and Engineering

Jainendra Pratap Singh

Department of Computer Science and Engineering

Swarup Kumar Misra

Department of Computer Science and Engineering

References

Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8),1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735

Abbasi, A., Sarker, A., & Chiang, R. (2017). A review of natural language processing in education. IEEE Transactions on Learning Technologies, 10(4), 349-377.

Brooks, C., & Wilson, J. T. (2013). Sentiment analysis on student evaluations of teaching: The utility of a web-based tool in medical education. JMIR Medical Education, 4(2), e13.

Chen, Y., & Xie, J. (2017). Online consumer review: Word-of-mouth as a new element of marketing communication mix. Management Science, 63(7), 2086-2102.

Poria, S., Cambria, E., & Hussain, A. (2016). A review of affective computing: From unimodal analysis to multimodal fusion. Information Fusion, 37, 98-125.

Goodfellow, Ian, et al. "Deep learning." Goodfellow et al. (2016).

Graves, A. (2013). Generating Sequences With Recurrent Neural Networks. arXiv preprint arXiv:1308.0850. https://arxiv.org/abs/1308.0850

A Systematic Spatial and Temporal Sentiment Analysis on Geo-Tweets

https://ieeexplore.ieee.org/document/8937540.

Multi-attention fusion modeling for sentiment analysis of educational big data

https://ieeexplore.ieee.org/document/9259196.

Knowledge-Guided Sentiment Analysis Via Learning From Natural Language Explanations https://ieeexplore.ieee.org/document/9316242.

Lexicon-Enhanced LSTM With Attention for General Sentiment Analysis

https://ieeexplore.ieee.org/document/8513826.

M. S. A. Vigil, M. M. Barhanpurkar, N. R. Anand, Y. Soni and A. Anand, "EYE SPY Face Detection and Identification using YOLO," 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2019, pp. 105-110, doi: 10.1109/ICSSIT46314.2019.8987830.

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8987830&isnumber=8987579

Vigil, MSA, Bharathi, VS. Detection of periodontal bone loss in mandibular area from dental panoramic radiograph using image processing techniques. Concurrency Computat Pract Exper. 2021;e6323. https://doi.org/10.1002/cpe.6323

Vigil, M.S.A., Bharathi, V.S. Classification of periodontitis stages in mandibular area from dental panoramic radiograph using Adaptive Center Line-Distance Based image processing approach. J Ambient Intell Human Comput 14, 8859–8869(2023). https://doi.org/10.1007/s12652-021-03634-7