Authors : Mahesh Kumar Chaudhary; Mahima Sahu; Manu Priya K; Pujashree V; Suguna A

Volume/Issue : Volume 9 - 2024, Issue 7 - July

Google Scholar : https://tinyurl.com/en4ptcps

DOI : https://doi.org/10.38124/ijisrt/24jul1852

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This project aims to meet the increasing need for real-time sentiment analysis within voice call interactions, acknowledging the rising significance of voice-based engagements in today's telecommunications realm. The proposed framework utilizes advanced natural language processing (NLP) techniques and machine learning models to promptly evaluate emotional nuances, integrating voice signal processing, feature extraction, and sentiment classification to ensure adaptability across diverse linguistic and cultural contexts. This initiative not only introduces a robust framework for real-time sentiment analysis but also tackles challenges specific to voice-based communication. Its wide-ranging applications span across industries such as customer service, market research, and social monitoring, offering valuable insights for organizations to comprehend and effectively respond to sentiments expressed within the dynamic landscape of real-time voice communication.

Keywords : Sentiment Analysis, NLP, NLTK, Voice to Text.

References :

1. Yang, L., Li, Y., Wang, J., & Sherratt, R. S. (2020). Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. *IEEE Access: Practical Innovations, Open Solutions, 8*, 23522–23530. https://doi.org/10.1109/access.2020.2969854
2. Kokane, C. D., Pathak, K. R., Mohadikar, G., Pagar, R. S., Chavan, S., & Kshirsagar, S. B. (2023). Machine learning-based sentiment analysis of incoming calls on helpdesk. *International Journal on Recent and Innovation Trends in Computing and Communication, 11*(9), 21–27. https://doi.org/10.17762/ijritcc.v11i9.8113
3. Li, S., Yerebakan, M. O., Luo, Y., Amaba, B., Swope, W., & Hu, B. (2022). The effect of different occupational background noises on voice recognition accuracy. *Journal of Computing and Information Science in Engineering, 22*(5). https://doi.org/10.1115/1.4053521
4. Deshmukh, S., & Gupta, P. (2023). Application of probabilistic neural network for speech emotion recognition. *International Journal of Speech Technology*. https://doi.org/10.1007/s10772-023-10037-w
5. Mutinda, J., Mwangi, W., & Okeyo, G. (2023). Sentiment analysis of text reviews using lexicon-enhanced Bert embedding (LeBERT) model with convolutional neural network. *Applied Sciences (Basel, Switzerland), 13*(3), 1445. https://doi.org/10.3390/app13031445
6. Mahgoub, A., Atef, H., Nasser, A., Yasser, M., Medhat, W. M., Darweesh, M. S., & El-Kafrawy, P. M. (2022). Sentiment analysis: Amazon electronics reviews using BERT and textblob. *2022 20th International Conference on Language Engineering (ESOLEC)*.
7. Gujjar, P., & Kumar, H. R. (n.d.). Sentiment Analysis: Textblob For Decision Making. *International Journal of Scientific Research & Engineering Trends, 7*(2), March-April-2021, ISSN (Online): 2395-566X.
8. BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text Chanjun Park1, Jaehyung Seo1, Seolhwa Lee1, Chanhee Lee1, Hyeonseok Moon1, Sugyeong Eo1, Heuiseok Lim1. (n.d.). https://doi.org/10.18653/v1/2021.wat-1.10
9. Li, J. (2022). Recent advances in end-to-end automatic speech recognition. *APSIPA Transactions on Signal and Information Processing, 11*(1). https://doi.org/10.1561/116.00000050