References
[1]. Akhtar, M. S., Kumar, A., Ekbal, A., & Bhattacharyya, P.
(2016). A hybrid deep learning architecture for sentiment
analysis. In Proceedings of COLING 2016, the 26th
International Conference on Computational Linguistics:
Technical Papers, 482-493.
[2]. Ali, M. A., & Kulkarn, S. B. (2021). Emotion detection
and sentiment analysis for hindi movie reviews.
International Journal of Emerging Trends & Technology in
Computer Science (IJETTCS), 10(1), 32-38.
[3]. Anusha, M., & Leelavathi, R. (2021). Analysis on
sentiment analytics using deep learning techniques. In
2021 Fifth International Conference on I-SMAC (IoT in
Social, Mobile, Analytics and Cloud) (I-SMAC), 542-547.
https://doi.org/10.1109/I-SMAC52330.2021.9640790
[4]. Artetxe, M., & Schwenk, H. (2019). Massively
multilingual sentence embeddings for zero-shot crosslingual
transfer and beyond. Transactions of the
Association for Computational Linguistics, 7, 597-610.
https://doi.org/10.1162/tacl_a_00288
[5]. Bansal, N., & Ahmed, U. Z. (n.d.). Sentiment analysis in
hindi. Indian Institute of Technology Kanpur, India, 1-10.
[6]. Byreddy, R. R., Malladi, S., Srikanth, B. V. S. S., &
Battula, V. (2022). Analysis of different methodologies for sentiment in hindi language. In Smart Intelligent
Computing and Applications, 1, 561-567. https://doi.org/10.1007/978-981-16-9669-5_51
[7]. Chachra, A., Mehndiratta, P., & Gupta, M. (2017).
Sentiment analysis of text using deep convolution neural
networks. In 2017 Tenth International Conference on
Contemporary Computing (IC3), 1-6. https://doi.org/10.1109/IC3.2017.8284327
[8]. Chakravarthi, B. R., Priyadharshini, R., Muralidaran,
V., Jose, N., Suryawanshi, S., Sherly, E., & McCrae, J. P.
(2022). Dravidiancodemix: Sentiment analysis and
offensive language identification dataset for Dravidian
languages in code-mixed text. Language Resources and
Evaluation, 56, 765-806. https://doi.org/10.1007/s10579-022-09583-7
[9]. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K.
(2018). Bert: Pre-training of deep bidirectional
transformers for language understanding. arXiv preprint
arXiv:1810.04805. https://doi.org/10.48550/arXiv.1810.04805
[10]. Goularas, D., & Kamis, S. (2019). Evaluation of deep
learning techniques in sentiment analysis from twitter
data. In 2019 International Conference on Deep
Learning and Machine Learning in Emerging
Applications (Deep-ML), 12-17. https://doi.org/10.1109/Deep-ML.2019.00011
[11]. Huang, X., Lin, N., Li, K., Wang, L., & Gan, S. (2021).
HinPLMs: Pre-trained language models for hindi. In 2021
International Conference on Asian Language Processing
(IALP), 241-246. https://doi.org/10.1109/IALP54817.2021.9675194
[12]. Joshi, R., Goel, P., & Joshi, R. (2020). Deep learning
for hindi text classification: A comparison. In International
Conference on Intelligent Human Computer Interaction,
11886, 94-101. https://doi.org/10.1007/978-3-030-44689-5_9
[13]. Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T.
(2016). Bag of tricks for efficient text classification. arXiv
preprint arXiv:1607.01759. https://doi.org/10.48550/arXiv.1607.01759
[14]. Khalil, E. A. H., El Houby, E. M., & Mohamed, H. K. (2020). Deep learning approach in sentiment analysis: a
review. In 2020 15th International Conference on
Computer Engineering and Systems (ICCES), 1-10.
https://doi.org/10.1109/ICCES51560.2020.9334625
[15]. Kulkarni, D. S., & Rodd, S. S. (2021). Sentiment
analysis in hindi—a survey on the state-of-the-art
techniques. Transactions on Asian and Low-Resource
Language Information Processing, 21(1), 1-46.
https://doi.org/10.1145/3469722
[16]. Lagrari, F., & Elkettani, Y. (2021). Traditional and deep
learning approaches for sentiment analysis: a survey.
Advances in Science, Technology and Engineering
Systems Journal, 6(5), 1-7. https://doi.org/10.25046/aj060501
[17]. Lai, S., Xu, L., Liu, K., & Zhao, J. (2015). Recurrent
convolutional neural networks for text classification. In
Twenty-Ninth AAAI Conference on Artificial Intelligence,
2267–2273.
[18]. Madan, A., & Ghose, U. (2021). Sentiment analysis
for twitter data in the hindi language. In 2021 11th
International Conference on Cloud Computing, Data
Science & Engineering (Confluence), 784-789.
https://doi.org/10.1109/Confluence51648.2021.9377142
[19]. Mehta, M., Pandey, U., Chaudhary, Y., Sharma, R.,
Gill, I., Gupta, D., & Khanna, A. (2021). Hindi text
classification: a review. In 2021 3rd International
Conference on Advances in Computing,
Communication Control and Networking (ICAC3N), 839-843. https://doi.org/10.1109/ICAC3N53548.2021.
9725517
[20]. Mohbey, K. K. (2021). Sentiment analysis for product
rating using a deep learning approach. In 2021
International Conference on Artificial Intelligence and
Smart Systems (ICAIS), 121-126. https://doi.org/10.1109/ICAIS50930.2021.9395802
[21]. Mukherjee, S. (2019). Deep learning technique for
sentiment analysis of hindi-english code-mixed text using
late fusion of character and word features. In 2019 IEEE
16th India Council International Conference (INDICON), 1-4. https://doi.org/10.1109/INDICON47234.2019.9028928
[22]. Nigam, S., Das, A. K., & Chandra, R. (2018). Machine
learning based approach to sentiment analysis. In 2018
International Conference on Advances in Computing,
Communication Control and Networking (ICACCCN),
157-161. https://doi.org/10.1109/ICACCCN.2018.8748848
[23]. Pennington, J., Socher, R., & Manning, C. D. (2014).
Glove: Global vectors for word representation. In
Proceedings of the 2014 Conference on Empirical
Methods in Natural Language Processing (EMNLP), 1532-1543.
[24]. Prabha, M. I., & Srikanth, G. U. (2019). Survey of
sentiment analysis using deep learning techniques. In
2019 1st International Conference on Innovations in
Information and Communication Technology (ICIICT), 1-9. https://doi.org/10.1109/ICIICT1.2019.8741438
[25]. Pradhan, R., & Sharma, D. K. (2022). An ensemble
deep learning classifier for sentiment analysis on codemix
Hindi–English data. Soft Computing, 1-18.
https://doi.org/10.1007/s00500-022-07091-y
[26]. Pundlik, S., Dasare, P., Kasbekar, P., Gawade, A.,
Gaikwad, G., & Pundlik, P. (2016). Multiclass classification
and class based sentiment analysis for Hindi language. In
2016 International Conference on Advances in
Computing, Communications and Informatics (ICACCI),
512-518. https://doi.org/10.1109/ICACCI.2016.7732097
[27]. Rakshitha, K., Ramalingam, H. M., Pavithra, M., Advi,
H. D., & Hegde, M. (2021). Sentimental analysis of Indian
regional languages on social media. Global Transitions
Proceedings, 2(2), 414-420. https://doi.org/10.1016/j.gltp.2021.08.039
[28]. Rani, S., & Kumar, P. (2019). Deep learning based
sentiment analysis using convolution neural network.
Arabian Journal for Science and Engineering, 44(4),
3305-3314. https://doi.org/10.1007/s13369-018-3500-z
[29]. Razali, N. A. M., Malizan, N. A., Hasbullah, N. A.,
Wook, M., Zainuddin, N. M., Ishak, K. K., ... & Sukardi, S.
(2021). Opinion mining for national security: techniques,
domain applications, challenges and research
opportunities. Journal of Big Data, 8(1), 1-46.
https://doi.org/10.1186/s40537-021-00536-5
[30]. Shalini, K., Ganesh, H. B., Kumar, M. A., & Soman, K. P.
(2018). Sentiment analysis for code-mixed Indian social
media text with distributed representation. In 2018
International Conference on Advances in Computing,
Communications and Informatics (ICACCI), 1126-1131.
https://doi.org/10.1109/ICACCI.2018.8554835
[31]. Sharma, A. (2020). Hindi text emotion recognition
based on deep learning. IOSR Journal of Mobile
Computing & Application (IOSR-JMCA), 7(3), 24-29.
https://doi.org/10.9790/0050-07032429
[32]. Sharma, R., Le Tan, N., & Sadat, F. (2018). Multimodal
sentiment analysis using deep learning. In 2018 17th IEEE
International Conference on Machine Learning and
Applications (ICMLA), 1475-1478. https://doi.org/10.1109/ICMLA.2018.00240
[33]. Sharma, Y., Mangat, V., & Kaur, M. (2015). A practical
approach to sentiment analysis of Hindi tweets. In 2015 1st
International Conference on Next Generation
Computing Technologies (NGCT), 677-680. https://doi.org/10.1109/NGCT.2015.7375207
[34]. Shilpa, P. C., Shereen, R., Jacob, S., & Vinod, P.
(2021). Sentiment analysis using deep learning. In 2021
Third International Conference on Intelligent
Communication Technologies and Virtual Mobile
Networks (ICICV), 930-937. https://doi.org/10.1109/ICICV50876.2021.9388382
[35]. Shrestha, H., Dhasarathan, C., Munisamy, S., &
Jayavel, A. (2020). Natural language processing based
sentimental analysis of Hindi (SAH) script an optimization
approach. International Journal of Speech Technology,
23(4), 757-766. https://doi.org/10.1007/s10772-020-09730-x
[36]. Shrivastava, K., & Kumar, S. (2020). A sentiment
analysis system for the hindi language by integrating
gated recurrent unit with genetic algorithm. International
Arab Journal of Information Technology, 17(6), 954-964.
[37]. Sindhu, C., Adak, S., & Tigga, S. C. (2021).
Opinionated text classification for hindi tweets using deep
learning. In 2021 5th International Conference on
Computing Methodologies and Communication
(ICCMC), 1217-1222. https://doi.org/10.1109/ICCMC51019.2021.9418361
[38]. Soni, V. K., & Selot, S. (2016). Part of speech tagging
approaches in hindi: a literature survey. BITCON.
[39]. Soni, V. K., & Selot, S. (2020). Big data for natural
language processing: a survey report. Proceedings of All
India Conference on Disruptive Technologies, 545-551.
[40]. Soni, V. K., & Selot, S. (2021a). Classification
technique approach in aspect based sentiment analysis:
a survey report. AICTE Sponsored National E-Conference
on Data Science and Its Applications.
[41]. Soni, V. K., & Selot, S. (2021b). A comprehensive
study for the hindi language to implement supervised text
classification techniques. In 2021 6th International
Conference on Signal Processing, Computing and
Control (ISPCC), 539-544. https://doi.org/10.1109/ISPCC53510.2021.9609401
[42]. Soong, H. C., Ayyasamy, R. K., & Akbar, R. (2021). A
review towards deep learning for sentiment analysis. In
2021 International Conference on Computer &
Information Sciences (ICCOINS), 238-243. https://doi.org/10.1109/ICCOINS49721.2021.9497233
[43]. Srinivasan, R., & Subalalitha, C. N. (2021).
Sentimental analysis from imbalanced code-mixed data
using machine learning approaches. Distributed and
Parallel Databases, 1-16. https://doi.org/10.1007/s10619-021-07331-4
[44]. Tummalapalli, M., Chinnakotla, M., & Mamidi, R. (2018). Towards better sentence classification for
morphologically rich languages. In Proceedings of the
International Conference on Computational Linguistics
and Intelligent Text Processing.
[45]. Wang, R., Li, Z., Cao, J., Chen, T., & Wang, L. (2019).
Convolutional recurrent neural networks for text
classification. In 2019 International Joint Conference on
Neural Networks (IJCNN), 1-6. https://doi.org/10.1109/IJCNN.2019.8852406
[46]. Wang, W. Y., Li, J., & He, X. (2018). Deep
reinforcement learning for NLP. In Proceedings of the 56th
Annual Meeting of the Association for Computational
Linguistics: Tutorial Abstracts, 19-21. https://doi.org/10.18653/v1/P18-5007
[47]. Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A
survey on sentiment analysis methods, applications, and
challenges. Artificial Intelligence Review, 55, 5731–5780.
https://doi.org/10.1007/s10462-022-10144-1
[48]. Zhang, L., Wang, S., & Liu, B. (2018). Deep learning
for sentiment analysis: A survey. Wiley Interdisciplinary
Reviews: Data Mining and Knowledge Discovery, 8(4).
https://doi.org/10.1002/widm
[49]. Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., & Xu, B.
(2016). Attention-based bidirectional long short-term
memor y networks for relation classification. In
Proceedings of the 54th Annual Meeting of the Association
for Computational Linguistics, 2, 207-212.