Pascal Muam Mah
ABSTRACT
Deep learning and natural language processing has emerged as modern machine with state-of-the-art modern learning and applicable techniques that help academicians, students, and teachers to identify, evaluate and validate documents and text features. Most educational systems have incorporated deep learning and natural language processing in fulfilling tasks except publishers and editorial boards, whom they handle important aspects of academic achievement in the decision-making for both journals and conferences. The aim is to introduce a standardized systematic peer review process based on factual justification, amendment, and recommendation approach on scholarly works free from emotional sentiments, politicization, bias, criticism, and condemnation. The study uses peer review feedback from journal XXXX to identify, evaluate and validate emotional sentimental aspects. A Confusion Metrix model was examined in the study to justify and validate the emotional sentiment pros and cons of the reviewer. Also, behavior-oriented drive and influential functions were used to convert text to value and rank the sentiment score. Each reviewer's text was converted into value using a behavior-oriented drive and influential function were used to examine the emotional sentiment level of the reviewers. Text mining and extraction of data uses a natural language process approach to identify sentiments from the reviewer's comments. Based on the confusion matrix and behavior-oriented drive, and influential function, the findings revealed limited significant emotional sentiment involved during the peer review process. The study concluded that the reviewers didn't perfectly validate the manuscript content due to minor emotional sentiments. The reviewer's evaluation of paper #Journal XXXX based on the findings exercise, few emotional sentiments were attracted during the review process. The study recommended editorial boards, journals, conferences, and publishing houses implement this approach to avoid emotional sentiments, politicization, and bias impact on academic evaluation, and validation.
- Adler, E., Hobbs, A., Dhaliwal, G., & Babik, J. M. (2020). Gender differences in authorship of clinical problem-solving articles. Journal of Hospital Medicine, 15(8), 475–478.Google Scholar
- Ain, Q. T., Ali, M., Riaz, A., Noureen, A., Kamran, M., Hayat, B., & Rehman, A. (2017). Sentiment analysis using deep learning techniques: a review. International Journal of Advanced Computer Science and Applications, 8(6).Google Scholar
- Alantari, H. J., Currim, I. S., Deng, Y., & Singh, S. (2022). An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews. International Journal of Research in Marketing, 39(1), 1-19.Google ScholarDigital Library
- An, J., Mendenhall, A., & Kaeberlein, M. (2023). The collaborative peer review framework as a model for training biomedical graduate students to perform rigorous, ethical peer review. Translational Medicine of Aging.Google Scholar
- Asuquo, G., & UBANI, S. (2022). NEGRITUDE AND AFRICAN IDENTITY IN CONTEMPORARY AFRICAN THOUGHT. AMAMIHE Journal of Applied Philosophy, 20(1).Google Scholar
- Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.Google Scholar
- Beasley, Z. J., Friedman, A., & Rosen, P. (2021). Through the Looking Glass: Insights Into Visualization Pedagogy Through Sentiment Analysis of Peer Review Text. IEEE Computer Graphics and Applications, 41(6), 59-70.Google ScholarCross Ref
- Canbek, G., Taskaya Temizel, T., & Sagiroglu, S. (2021). BenchMetrics: A systematic benchmarking method for binary classification performance metrics. Neural Computing and Applications, 33(21), 14623-14650.Google ScholarDigital Library
- Chakraborty, S., Goyal, P., & Mukherjee, A. (2020, August). Aspect-based sentiment analysis of scientific reviews. In Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 (pp. 207-216).Google ScholarDigital Library
- Choi, G., Oh, S., & Kim, H. (2020). Improving document-level sentiment classification using importance of sentences. Entropy, 22(12), 1336.Google ScholarCross Ref
- Dou, Z. Y. (2017, September). Capturing user and product information for document level sentiment analysis with deep memory network. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 521-526).Google Scholar
- Edalati, M., Imran, A. S., Kastrati, Z., & Daudpota, S. M. (2022). The potential of machine learning algorithms for sentiment classification of students’ feedback on MOOC. In Intelligent Systems and Applications: Proceedings of the 2021 Intelligent Systems Conference (IntelliSys) Volume 3 (pp. 11-22). Springer International Publishing.Google Scholar
- Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89.Google Scholar
- Ghosal, T., Kumar, S., Bharti, P. K., & Ekbal, A. (2022). Peer review analyze: A novel benchmark resource for computational analysis of peer reviews. Plos one, 17(1), e0259238.Google Scholar
- Hoge, R. D., & Coladarci, T. (1989). Teacher-based judgments of academic achievement: A review of literature. Review of educational research, 59(3), 297-313.Google Scholar
- Huber, J., Inoua, S., Kerschbamer, R., König-Kersting, C., Palan, S., & Smith, V. L. (2022). Nobel and novice: Author prominence affects peer review. Proceedings of the National Academy of Sciences, 119(41), e2205779119.Google Scholar
- International Journal on Digital Libraries, 1-19.Google Scholar
- Iyoha, E. S., Aluko, J. O., & Ohwojevwe, E. G. (2018). Modern Drama: Signpost to Evolution Development of Science and Technology in Nigeria Polytechnics. S/N Contents, 1.Google Scholar
- Joseph-Edwards, A. (2023). The Perceptions and Use of Exemplars and Peer Feedback in Student Revisions. Caribbean Journal of Multidisciplinary Studies, 2(1), 45-64.Google Scholar
- Koczanowicz, D. (2009). COMMUNITY, INDIVIDUALITY, AND EXPERIENCE. In Self and Society (pp. 181-189). Brill.Google Scholar
- Kumar, A., & Garg, G. (2019). Sentiment analysis of multimodal twitter data. Multimedia Tools and Applications, 78, 24103-24119.Google Scholar
- Kumar, S., Ghosal, T., & Ekbal, A. (2023). DeepMetaGen: an unsupervised deep neural approach to generate template-based meta-reviews leveraging on aspect category and sentiment analysis from peer reviews. Google Scholar
- Le, Q., & Mikolov, T. (2014, June). Distributed representations of sentences and documents. In International conference on machine learning (pp. 1188-1196). PMLR.Google Scholar
- Levy, J. J., & O'Malley, A. J. (2020). Don't dismiss logistic regression: the case for sensible extraction of interactions in the era of machine learning. BMC medical research methodology, 20(1), 1-15.Google Scholar
- Li, D., Yin, H., Wang, C., Song, S., Li, K., & Li, C. (2022). Visual Recommendation for Peer-To-Peer Accommodation with Online Reviews based on Sentiment Analysis and Topic Models. Journal of Visualization, 25(6), 1309-1327.Google ScholarDigital Library
- Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1-167.Google Scholar
- Lou, Y., Zhang, Y., Li, F., Qian, T., & Ji, D. (2020). Emoji-based sentiment analysis using attention networks. ACM Transactions on asian and low-resource language information processing (TALLIP), 19(5), 1-13.Google Scholar
- Luo, J., Feliciani, T., Reinhart, M., Hartstein, J., Das, V., Alabi, O., & Shankar, K. (2021). Analyzing sentiments in peer review reports: Evidence from two science funding agencies. Quantitative Science Studies, 2(4), 1271-1295.Google ScholarCross Ref
- Malachowski, A. (2018). Rorty and the meaning of life. In The Meaning of Life and the Great Philosophers (pp. 266-273). Routledge.Google ScholarCross Ref
- Malviya, S., Tiwari, A. K., Srivastava, R., & Tiwari, V. (2020). Machine learning techniques for sentiment analysis: A review. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 12(02), 72-78.Google Scholar
- Moraes, R., Valiati, J. F., & Neto, W. P. G. (2013). Document-level sentiment classification: An empirical comparison between SVM and ANN. Expert Systems with Applications, 40(2), 621-633.Google Scholar
- Morales-Trujillo, M. E., Galster, M., Gilson, F., & Mathews, M. (2021). A Three-Year Study on Peer Evaluation in a Software Engineering Project Course. IEEE Transactions on Education, 65(3), 409-418.Google ScholarDigital Library
- Moriguchi, Y. (2022). Beyond bias to Western participants, authors, and editors in developmental science. Infant and Child Development, 31(1), e2256.Google Scholar
- Mukerjee, J., & Metiu, A. (2022). Play and psychological safety: An ethnography of innovative work. Journal of Product Innovation Management, 39(3), 394-418.Google ScholarCross Ref
- Nigam, N., & Yadav, D. (2018). Lexicon-based approach to sentiment analysis of tweets using R language. In Advances in Computing and Data Sciences: Second International Conference, ICACDS 2018, Dehradun, India, April 20-21, 2018, Revised Selected Papers, Part I 2 (pp. 154-164). Springer Singapore.Google ScholarCross Ref
- Obi, J. C. (2023). A comparative study of several classification metrics and their performances on data. World Journal of Advanced Engineering Technology and Sciences, 8(1), 308-314.Google ScholarCross Ref
- Parekh, R., Patel, N. P., Thakkar, N., Gupta, R., Tanwar, S., Sharma, G., ... & Sharma, R. (2022). DL-GuesS: Deep learning and sentiment analysis-based cryptocurrency price prediction. IEEE Access, 10, 35398-35409.Google ScholarCross Ref
- Poria, S., Cambria, E., & Gelbukh, A. (2016). Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems, 108, 42-49.Google Scholar
- Prasetyo, A. R., Sayono, J., & Rahmawati, N. (2022). Design of a Visual Identity to Build Branding of a Rattan Weaving Tourism Village in Malang City. KnE Social Sciences, 62-69.Google ScholarCross Ref
- Preston, T. (2006). The Private and Public Appeal of Self-Fashioning. Journal of Nietzsche Studies, 10-19.Google ScholarCross Ref
- Revathy, G., Alghamdi, S. A., Alahmari, S. M., Yonbawi, S. R., Kumar, A., & Haq, M. A. (2022). Sentiment analysis using machine learning: Progress in the machine intelligence for data science. Sustainable Energy Technologies and Assessments, 53, 102557.Google Scholar
- Ribeiro, A. C., Sizo, A., Lopes Cardoso, H., & Reis, L. P. (2021). Acceptance decision prediction in peer-review through sentiment analysis. In Progress in Artificial Intelligence: 20th EPIA Conference on Artificial Intelligence, EPIA 2021, Virtual Event, September 7–9, 2021, Proceedings 20 (pp. 766-777). Springer International Publishing.Google ScholarDigital Library
- Rowley, J., Sbaffi, L., Sugden, M., & Gilbert, A. (2022). Factors influencing researchers’ journal selection decisions. Journal of Information Science, 48(3), 321-335.Google Scholar
- Santamaría, L., & Mihaljević, H. (2018). Comparison and benchmark of name-to-gender inference services. PeerJ Computer Science, 4, e156.Google Scholar
- Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., & Potts, C. (2013, October). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 conference on empirical methods in natural language processing (pp. 1631-1642).Google Scholar
- Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., & Potts, C. (2013, October). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 conference on empirical methods in natural language processing (pp. 1631-1642).Google Scholar
- Suttles, J., & Ide, N. (2013). Distant supervision for emotion classification with discrete binary values. In Computational Linguistics and Intelligent Text Processing: 14th International Conference, CICLing 2013, Samos, Greece, March 24-30, 2013, Proceedings, Part II 14 (pp. 121-136). Springer Berlin Heidelberg.Google ScholarDigital Library
- Tang, D. (2015, February). Sentiment-specific representation learning for document-level sentiment analysis. In Proceedings of the eighth ACM international conference on web search and data mining (pp. 447-452).Google ScholarDigital Library
- Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American society for information science and technology, 61(12), 2544-2558.Google Scholar
- Tricco, A. C., Khalil, H., Holly, C., Feyissa, G., Godfrey, C., Evans, C., ... & Munn, Z. (2022). Rapid reviews and the methodological rigor of evidence synthesis: a JBI position statement. JBI evidence synthesis, 20(4), 944-949.Google Scholar
- Wang, K., & Wan, X. (2018, June). Sentiment analysis of peer review texts for scholarly papers. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (pp. 175-184).Google ScholarDigital Library
- Xu, J., Chen, D., Qiu, X., & Huang, X. (2016). Cached long short-term memory neural networks for document-level sentiment classification. arXiv preprint arXiv:1610.04989.Google Scholar
- Xu, J., Chen, D., Qiu, X., & Huang, X. (2016). Cached long short-term memory neural networks for document-level sentiment classification. arXiv preprint arXiv:1610.04989.Google Scholar
- Yadav, A., & Vishwakarma, D. K. (2020). Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review, 53(6), 4335-4385.Google ScholarDigital Library
- Zhang, L., Shang, Y., Huang, Y., & Sivertsen, G. (2022). Gender differences among active reviewers: an investigation based on Publons. Scientometrics, 127(1), 145-179.Google Scholar
- Zhang, Z., Li, J., Zhu, P., Zhao, H., & Liu, G. (2018). Modeling multi-turn conversation with deep utterance aggregation. arXiv preprint arXiv:1806.09102.Google Scholar
Index Terms
- The art of deep learning and natural language processing for emotional sentiment analysis on the academic scholars' peer review process.
Index terms have been assigned to the content through auto-classification.
Recommendations
Natural Language Processing for Sentiment Analysis: An Exploratory Analysis on Tweets
ICAIET '14: Proceedings of the 2014 4th International Conference on Artificial Intelligence with Applications in Engineering and TechnologyIn this paper, we present our preliminary experiments on tweets sentiment analysis. This experiment is designed to extract sentiment based on subjects that exist in tweets. It detects the sentiment that refers to the specific subject using Natural ...
Sentiment-Specific Representation Learning for Document-Level Sentiment Analysis
WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data MiningIn this paper, we propose a representation learning research framework for document-level sentiment analysis. Given a document as the input, document-level sentiment analysis aims to automatically classify its sentiment/opinion (such as thumbs up or ...
Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion
Highlights- A novel deep-learning-based method for sentiment classification.
- Rich feature ...
AbstractSentiment analysis concerns the study of opinions expressed in a text. Due to the huge amount of reviews, sentiment analysis plays a basic role to extract significant information and overall sentiment orientation of reviews. In this ...
Comments