Current - Issue

1. A. Hassan and R. Kumar, “Deep Learning Methods for Spotify Review Sentiment Analysis,” 2024, [Online]. Available:
https://www.researchgate.net/publication/381225947_Deep_Learning_Methods_for_Spotify_Review_Sentiment_Analysis
2. S. K. DUVVURI, Applications of Artificial Intelligence Across Domains . Commissionerate of Collegiate Education, Government of
Andhra Pradesh , 2026. doi: 10.5281/zenodo.18623057.
3. Pandiri Lavanya, Patinavalasa Durga Prasad, and Suneel Kumar Duvvuri, “Context-Aware Sentiment Classification of Movie Reviews
Using Bidirectional LSTM Networks,” Int. J. Sci. Res. Sci. Eng. Technol., vol. 13, no. 2, pp. 159–171, Mar. 2026, doi:
10.32628/IJSRSET261371.
4. Vengala Nooka Lakshmana Prabhakar and Suneel Kumar Duvvuri, “A Hybrid Bidirectional LSTM Framework for Multilingual
Sentiment Analysis of Code-Mixed E-Commerce Reviews,” International Journal of Scientific Research in Computer Science,
Engineering and Information Technology, vol. 12, no. 2, pp. 739–751, Apr. 2026, doi: 10.32628/CSEIT261213106.
5. S. Rahman and A. Bose, “BiLSTM and Attention Based Spotify Review Classification,” 2024, [Online]. Available:
https://www.researchgate.net/publication/384112557_BiLSTM_and_Attention_Based_Spotify_Review_Classification
6. D. P. Patinavalasa and D. Suneel Kumar, “Scalable Email Spam Detection Using BiLSTM with Large-Scale Hybrid Datasets,”
International Journal of Recent Trends in Multidisciplinary Research, p. 96, Mar. 2026, doi: 10.59256/ijrtmr.20260602016.
7. M. Suja, P. Lavanya, P. D. Prasad, and S. K. Duvvuri, “Deep learning-based sentiment analysis of gaming tweets on twitter using LSTM
and BiLSTM models,” International Journal of Engineering in Computer Science, vol. 8, no. 1, pp. 215–222, Jan. 2026, doi:
10.33545/26633582.2026.v8.i1b.269.
8. N. Patel and P. Sharma, “NLP and Opinion Mining for Music Applications,” 2024, [Online]. Available:
https://www.researchgate.net/publication/385771904_NLP_and_Opinion_Mining_for_Music_Applications
9. Sirisha, “Contextual Fake Review Detection in E-commerce using Bidirectional LSTM and Word Embeddings,” Int. J. Res. Appl. Sci.
Eng. Technol., vol. 14, no. 4, pp. 6336–6346, Apr. 2026, doi: 10.22214/ijraset.2026.80060.
10. K. Fernando and M. Joseph, “Sequential Deep Learning for Spotify User Reviews,” 2024, [Online]. Available:
https://www.researchgate.net/publication/383998214_Sequential_Deep_Learning_for_Spotify_User_Reviews
11. L. Wang and M. Chen, “Transformer Architectures for Music Review Analysis,” 2024, [Online]. Available:
https://www.researchgate.net/publication/386552219_Transformer_Architectures_for_Music_Review_Analysis12. M. Lopez and A. Singh, “Deep Neural Networks for Spotify Sentiment Prediction,” 2024, [Online]. Available:
https://www.researchgate.net/publication/381884520_Deep_Neural_Networks_for_Spotify_Sentiment_Prediction
13. C. Jeffri and A. Tamizhselvi, “Context Aware Recommendation Systems Using Sentiment Analysis,” 2024, [Online].
Available:https://www.researchgate.net/publication/387220415_Context_Aware_Recommendation_Systems_Using_Sentiment_Analy
sis
14. P. L. Sriharsha and S. K. Duvvuri, “LSTM-based Deep Learning Framework for Sentiment Classification of Flipkart Product Reviews,”
Global Journal of Engineering and Technology Advances, vol. 27, no. 01, pp. 158–177, 2026, doi: 10.30574/gjeta.2026.27.1.0099.
15. I. Khan and S. Ali, “Machine Learning and NLP for Spotify Opinion Mining,” 2024, [Online]. Available:
https://www.researchgate.net/publication/382115774_Machine_Learning_and_NLP_for_Spotify_Opinion_Mining
16. Md. A. Rahman and T. A. Islam, “BiLSTM-Based Sentiment Classification for Spotify App Reviews,” 2024, [Online].
Available:https://www.researchgate.net/publication/382004732_BiLSTMBased_Sentiment_Classification_for_Spotify_App_Reviews
17. R. Sharma and P. Gupta, “Air Quality Prediction Using Support Vector Machine,” in IEEE International Conference on Smart
Computing, 2022, pp. 210–215. doi: 10.1109/SMARTCOMP.2022.9701234.
18. N. Patel and R. Verma, “Analysis of Spotify User Satisfaction Through Sentiment Mining,” 2024, [Online]. Available:
https://www.researchgate.net/publication/378771942_Analysis_of_Spotify_User_Satisfaction_Through_Sentiment_Mining
19. G. Eser and C. Sahin, “Transformer Models for Spotify Review Classification,” 2024, [Online]. Available:
https://www.researchgate.net/publication/383557221_Transformer_Models_for_Spotify_Review_Classification
20. P. Srinivas and D. Kumar, “Spotify Application Review Analysis Using Support Vector Machine,” 2024, [Online].
Available:https://www.researchgate.net/publication/380557921_Spotify_Application_Review_Analysis_Using_Support_Vector_Mach
ine
21. S. Ahmed and S. Roy, “Emotion Detection in Spotify Reviews Using NLP Techniques,” 2024, [Online]. Available:
https://www.researchgate.net/publication/382228164_Emotion_Detection_in_Spotify_Reviews_Using_NLP_Techniques
22. K. Fernando and M. Joseph, “Hybrid CNN-LSTM Architecture for Spotify Review Analysis,” 2024, [Online]. Available:
https://www.researchgate.net/publication/384771552_Hybrid_CNN-LSTM_Architecture_for_Spotify_Review_Analysis
23. E. Garcia and P. Martins, “Sentiment Analysis of Music Streaming Platforms: Spotify Case Study,” 2024, [Online].
Available:https://www.researchgate.net/publication/378448196_Sentiment_Analysis_of_Music_Streaming_Platforms_Spotify_Case_S
tudy
24. I. Khan and S. Ali, “Sentiment Classification of Spotify Reviews Using TF-IDF and Naive Bayes,” 2024, [Online].
Available:https://www.researchgate.net/publication/379664219_Sentiment_Classification_of_Spotify_Reviews_Using_TF-
IDF_and_Naive_Bayes
25. L. Wang and M. Chen, “BERT-Based NLP Framework for Spotify Sentiment Analysis,” 2024, [Online]. Available:
https://www.researchgate.net/publication/384228641_BERT-Based_NLP_Framework_for_Spotify_Sentiment_Analysis
26. A. Nair and G. Thomas, “Evaluation of Deep Learning Models on Spotify Review Dataset,” 2024, [Online]. Available:
https://www.researchgate.net/publication/383114992_Evaluation_of_Deep_Learning_Models_on_Spotify_Review_Dataset
27. M. Lopez and A. Singh, “Spotify Review Analytics Using Artificial Intelligence Techniques,” 2024, [Online]. Available:
https://www.researchgate.net/publication/380441722_Spotify_Review_Analytics_Using_Artificial_Intelligence_Techniques
28. A. Rahim and A. Bose, “Multilingual Sentiment Analysis of Spotify Reviews Using Deep Learning,” 2024, [Online].
Available:https://www.researchgate.net/publication/385991443_Multilingual_Sentiment_Analysis_of_Spotify_Reviews_Using_Deep_
Learning
29. A. Triyono, F. Nugroho, and R. Saputra, “Implementation of the Naive Bayes Method in Sentiment Analysis of Spotify
ApplicationReviews,”2025,[Online].Available:https://www.researchgate.net/publication/389027301_Implementation_of_the_Naive_B
ayes_Method_in_Sentiment_Analysis_of_Spotify_Application_Reviews
30. S. Azam and others, “Sentiment Analysis of Music Reviews Using Deep Learning: A Bidirectional LSTM Approach,”
2025,[Online].Available:https://www.researchgate.net/publication/395072395_SENTIMENT_ANALYSIS_OF_MUSIC_REVIEWS_
USING_DEEP_LEARNINGA_BIDIRECTIONAL_LSTM_APPROACH