Abstract
Each language is a system of understanding and skills that allows language users to interact, express thoughts, hypotheses, feelings, wishes, and all that needs to be expressed. Linguistics is the research of these structures in all respects: the composition, usage, and sociology of language, in particular, are the core of linguistics. Machine Learning is the research area that allows machines to learn without being specifically scheduled. In linguistics, the design of writing is understood to be a foundation for many distinct company apps and probably the most useful if incorporated with machine learning methods. Research shows that besides text tagging and algorithm training, there are major problems in the field of Big Data. This article provides a collaborative effort (transfer learning integrated into Recurrent Neural Network) to analyze the distinct kinds of writing between the language's linear and non-computational sides, and to enhance granularity. The outcome demonstrates stronger incorporation of granularity into the language from both sides. Comparative results of machine learning algorithms are used to determine the best way to analyze and interpret the structure of the language.
- A. Abdi, S. M. Shamsuddin, S. Hasan, and J. Piran. 2018. Machine learning-based multi-documents sentiment-oriented summarization using linguistic treatment. Expert Systems with Applications 109, 66–85. https://doi.org/10.1016/j.eswa.2018.05.010Google Scholar
Digital Library
- A. Abdi, S. M. Shamsuddin, and R. M. Aliguliyev. 2018. QMOS: Query-based multi-documents opinion-oriented summarization. Information Processing and Management 54, 2 (2018), 318–338.Google Scholar
Cross Ref
- Y. Asim, A. R. Shahid, A. K. Malik, and B. Raza. 2018. Significance of machine learning algorithms in professional blogger's classification. Computers and Electrical Engineering, 65, 461–473. https://doi.org/10.1016/j.compeleceng.2017.08.001Google Scholar
Cross Ref
- L. Chen, C. P. Chen, and W. Pedrycz. 2009. A gradient-descent-based approach for transparent linguistic interface generation in fuzzy models. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 40, 5 (2009), 1219–1230. Google Scholar
Digital Library
- D. Gruda and S. Hasan. 2019. Feeling anxious? Perceiving anxiety in tweets using machine learning. Computers in Human Behavior 98, 245–255. https://doi.org/10.1016/j.chb.2019.04.020Google Scholar
Cross Ref
- H. He, T. M. McGinnity, S. Coleman, and B. Gardiner. 2013. Linguistic decision making for robot route learning. IEEE Transactions on Neural Networks and Learning Systems 25, 1 (2013), 203–215.Google Scholar
Cross Ref
- P. M. Kumar, U. Gandhi, R. Varatharajan, G. Manogaran, R. Jidhesh, and T. Vadivel. 2019. Intelligent face recognition and navigation system using neural learning for smart security in internet of things. Cluster Computing 22, 4 (2019), 7733–7744.Google Scholar
Digital Library
- R. C. Kessler, R. M. Bossarte, A. Luedtke, A. M. Zaslavsky, and J. R. Zubizarreta. 2019. Machine learning methods for developing precision treatment rules with observational data. Behaviour Research and Therapy 120, 103412. https://doi.org/10.1016/j.brat.2019.103412Google Scholar
Cross Ref
- K. Kaczmarek-Majer and O. Hryniewicz. 2019. Application of linguistic summarization methods in time series forecasting. Information Sciences, 478, 580–594. https://doi.org/10.1016/j.ins.2018.11.036Google Scholar
Cross Ref
- M. Li, R. Jiang, S. S. Ge, and T. H. Lee. 2018. Role playing learning for socially concomitant mobile robot navigation. CAAI Transactions on Intelligence Technology 3, 1 (2018), 49–58.Google Scholar
Cross Ref
- H. Leopold, H. van der Aa, J. Offenberg, and H. A. Reijers. 2019. Using hidden Markov models for the accurate linguistic analysis of process model activity labels. Information Systems 83, 30–39. https://doi.org/10.1016/j.is.2019.02.005Google Scholar
Cross Ref
- Y. Liu, Y. Wan, and X. Su. 2019. Identifying individual expectations in service recovery through natural language processing and machine learning. Expert Systems with Applications, 131, 288–298. https://doi.org/10.1016/j.eswa.2019.04.063Google Scholar
Cross Ref
- N. S. Murugan and G. U. Devi. 2019. Feature extraction using LR-PCA hybridization on Twitter data and classification accuracy using machine learning algorithms. Cluster Computing 22, 6 (2019), 13965–13974.Google Scholar
Cross Ref
- G. Manogaran, V. Vijayakumar, R. Varatharajan, P. M. Kumar, R. Sundarasekar, and C. H. Hsu. 2018. Machine learning based big data processing framework for cancer diagnosis using hidden Markov model and GM clustering. Wireless Personal Communications 102, 3 (2018), 2099–2116. Google Scholar
Digital Library
- G. Manogaran, P. M. Shakeel, A. S. Hassanein, P. M. Kumar, and G. C. Babu. 2018. Machine learning approach-based gamma distribution for brain tumor detection and data sample imbalance analysis. IEEE Access 7, 12–19. DOI:10.1109/ACCESS.2018.2878276Google Scholar
Cross Ref
- Y. V. Nieto, V. García-Díaz, and C. E. Montenegro. 2019. Decision-making model at higher educational institutions based on machine learning. Journal of Universal Computer Science 25, 10 (2019), 1301–1322.Google Scholar
- Y. Nieto, V. García-Díaz, C. Montenegro, and R. G. Crespo. 2019. Supporting academic decision making at higher educational institutions using machine learning-based algorithms. Soft Computing 23, 12 (2019), 4145–4153. Google Scholar
Digital Library
- A. Neviarouskaya and M. Aono. 2013. Sentiment word relations with affect, judgment, and appreciation. IEEE Transactions on Affective Computing 4, 4 (2013), 425–438.Google Scholar
Cross Ref
- M. Ott, Y. Choi, C. Cardie, and J. T. Hancock. 2011. Finding deceptive opinion spam by any stretch of the imagination. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-volume 1 (309–319). ACL. Google Scholar
Digital Library
- N. Quadrianto, A. J. Smola, T. S. Caetano, and Q. V. Le. 2009. Estimating labels from label proportions. Journal of Machine Learning Research, 10(Oct), 2349–2374. Google Scholar
Digital Library
- Retrieved from http://opennlp.apache.org/.Google Scholar
- S. Selvakumar, H. Inbarani, and P. M. Shakeel. 2016. A hybrid personalized tag recommendations for social e-learning system. International Journal of Control Theory and Applications 9, 2 (2016), 1187–1199.Google Scholar
- P. M. Shakeel and S. Baskar. 2020. Automatic human emotion classification in web document using fuzzy inference system (FIS): Human emotion classification. International Journal of Technology and Human Interaction (IJTHI) 16, 1 (2020), 94–104.Google Scholar
Cross Ref
- N. A. Smith and A. F. Martins. 2013. Linguistic structure prediction with the sparseptron. XRDS: Crossroads, The ACM Magazine for Students, 19, 3 (2013), 44–48. Google Scholar
Digital Library
- S. A. Vermeer, T. Araujo, S. F. Bernritter, and G. van Noort. 2019. Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media. International Journal of Research in Marketing 36, 3 (2019), 492–508.Google Scholar
Cross Ref
- J. Wu and X. Xu. 2018. Decentralised grid scheduling approach based on multi-agent reinforcement learning and gossip mechanism. CAAI Transactions on Intelligence Technology 3, 1 (2018), 8–17.Google Scholar
Cross Ref
- M. F. Weng and Y. Y. Chuang. 2011. Cross-domain multicue fusion for concept-based video indexing. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 10 (2011), 1927–1941. Google Scholar
Digital Library
- Q. Xu and H. Zhao. 2012. Using deep linguistic features for finding deceptive opinion spam. In Proceedings of COLING 2012: Posters (1341–1350).Google Scholar
- X. Yuan, D. Li, D. Mohapatra, and M. Elhoseny. 2018. Automatic removal of complex shadows from indoor videos using transfer learning and dynamic thresholding. Computers and Electrical Engineering 70, 813–825.Google Scholar
Cross Ref
- M. Yang, Q. Jiang, Y. Shen, Q. Wu, Z. Zhao, and W. Zhou. 2019. Hierarchical human-like strategy for aspect-level sentiment classification with sentiment linguistic knowledge and reinforcement learning. Neural Networks 117, 240--248. https://doi.org/10.1016/j.neunet.2019.05.021Google Scholar
Digital Library
Index Terms
Transfer Learning Based Recurrent Neural Network Algorithm for Linguistic Analysis
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