Efficient Sentimental Analysis using Hybrid Deep Transfer Learning Neural Network

Efficient Sentimental Analysis using Hybrid Deep Transfer Learning Neural Network

  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-10
Year of Publication : 2022
Authors : K. Kavitha, Suneetha Chittineni
DOI : 10.14445/22315381/IJETT-V70I10P216

How to Cite?

K. Kavitha, Suneetha Chittineni, "Efficient Sentimental Analysis using Hybrid Deep Transfer Learning Neural Network," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 155-165, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P216

Abstract
Sentiment analysis has become a famous exploration theme for recovering important data from different online conditions. Most existing sentiment examines depend on the managed realization that supervised learning requires an adequate measure of marked information. Be that as it may, sentiment analysis frequently faces deficient named information, practically speaking, as it is pricey and tedious to mark the enormous measure of information. To deal with the lack of beginning named information, this article presented a deep and effective method for sentiment analysis through a hybrid deep-learning neural network. Unstructured data is first converted into a structured format and then pre-processed through tokenization, stop word removal, and the weighting factor. According to the retrieved sentiment features, such as emotion, personality traits, and demographic features within the pre-processed data, a deep learning neural network classifies the data for sentiment analysis. SGOA (Support-based Grasshopper Optimization Algorithm) is used to tune the weight parameter of each layer of the suggested Hybrid Deep Transfer Learning Neural Network (HDNN). At last, performance compared with these existing models, the proposed model achieves the highest analysis value.

Keywords
Sentiment Analysis, HDNN, Tokenization, Stop Word Removal, Weighting Factor, Support Value, Grasshopper Optimisation Algorithm.

Reference
[1] Ghosh, Monalisa, and Goutam Sanyal, "Performance Assessment of Multiple Classifiers Based on Ensemble Feature Selection Schemefor Sentiment Analysis," Applied Computational Intelligence and Soft Computing, vol. 2018, 2018.
[2] Ghosh, Monalisa, and Goutam Sanyal, "An Ensemble Approach to Stabilize the Features for Multi-Domain Sentiment Analysis using Supervised Machine Learning," Journal of Big Data, vol. 5, no. 1, pp. 44, 2018.
[3] Philander, Kahlil, and Y. Zhong, "Twitter Sentiment Analysis: Capturing Sentiment from Integrated Resort Tweets," International Journal of Hospitality Management, vol. 55, no. 2016, pp. 16-24, 2016.
[4] El Alaoui, Imane, Youssef Gahi, Rochdi Messoussi, Youness Chaabi, Alexis Todoskoff, and Abdessamad Kobi, "A Noveladaptable Approach for Sentiment Analysis on Big Social Data," Journal of Big Data, vol. 5, no. 1, pp. 12, 2018.
[5] Luo, Jiaqi, Songshan Huang, and Renwu Wang, "A Fine-Grained Sentiment Analysis of Online Guest Reviews of Economy Hotels Inchina," Journal of Hospitality Marketing & Management, no. 2020, pp. 1-25.
[6] Li, Zuhe, Yangyu Fan, Bin Jiang, Tao Lei, and Weihua Liu, "A Survey on Sentiment Analysis and Opinion Mining for Social Multimedia," Multimedia Tools and Applications, vol. 78, no. 6, pp. 6939-6967, 2019.
[7] Kumar, Akshi, and Geetanjali Garg, "The Multifaceted Concept of Context in Sentiment Analysis," In Cognitive Informatics and Soft Computing, Springer, Singapore, pp 413-421, 2020.
[8] Ribeiro, Filipe N., Matheus Araújo, Pollyanna Gonçalves, Marcos André Gonçalves, and Fabrício Benevenuto, "Sentibench-A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods," EPJ Data Science, vol. 5, no. 1, pp. 1-29, 2016.
[9] Mohammad, Saif M, "Sentiment Analysis: Detecting Valence, Emotions, and Other Effectual States from the Text," In Emotion Measurement, Woodhead Publishing, pp. 201-237, 2016.
[10] Moraes, Rodrigo, JoãO Francisco Valiati, and Wilson P. GaviãO Neto, "Document-Level Sentiment Classification: An Empiricalcomparison between SVM and ANN," Expert Systems with Applications, vol. 40, no. 2, pp. 621-633, 2013.
[11] Vinodhini, G., and R. M. Chandrasekaran, "Sentiment Analysis and Opinion Mining: A Survey," International Journal, vol. 2, no. 6, pp. 282-292, 2012.
[12] Manek, Asha S., P. Deepa Shenoy, M. Chandra Mohan, and K. R. Venugopal, "Aspect Term Extraction for Sentiment Analysis Inlarge Movie Reviews using Gini Index Feature Selection Method and SVM Classifier," World Wide Web, vol. 20, no. 2, pp. 135- 154, 2017.
[13] Tang, Duyu, Bing Qin, and Ting Liu, "Document Modeling with Gated Recurrent Neural Network for Sentiment Classification," In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1422-1432, 2015.
[14] Pradhan, Vidisha M., Jay Vala, and Prem Balani, "A Survey on Sentiment Analysis Algorithms for Opinion Mining," International Journal of Computer Applications, vol. 133, no. 9, pp. 7-11, 2016.
[15] Liu, Shu, Wei Li, Yunfang Wu, Qi Su, and Xu Sun, "Jointly Modeling Aspect and Sentiment with Dynamic Heterogeneous GraphNeural Networks," arXiv preprint arXiv: 2004.06427, 2020.
[16] Afreen Jaha, N.Satya Deepthi, G.Suryakanth, G. Surya Kala Eswari, "Text Sentiment Analysis Using Naïve Baye's Classifier," International Journal of Computer Trends and Technology, vol. 68, no. 4, pp. 261-265, 2020. https://doi.org/10.14445/22312803/IJCTT-V68I4P141
[17] Liu, Bing. "Sentiment Analysis and Opinion Mining," Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1- 167, 2012.
[18] Quan, Changqing, and Fuji Ren, "Unsupervised Product Feature Extraction for Feature-Oriented Opinion Determination," Information Sciences, vol. 272, pp. 16-28, 2014.
[19] Smailović, Jasmina, Miha Grčar, Nada Lavrač, and Martin Žnidaršič, "Stream-Based Active Learning for Sentiment Analysis in the Financial Domain," Information Sciences, vol. 285, pp. 181-203, 2014.
[20] Catal, Cagatay, and Mehmet Nangir, "A Sentiment Classification Model Based on Multiple Classifiers," Applied Soft Computing, vol. 50, pp. 135-141, 2017.
[21] Yao, Fang, and Yan Wang, "Domain-Specific Sentiment Analysis for Tweets During Hurricanes (DSSA-H): A DomainAdversarial Neural-Network-Based Approach," Computers, Environment and Urban Systems, vol. 83, pp. 101522, 2020.
[22] Hassonah, Mohammad A., Rizik Al-Sayyed, Ali Rodan, Al-Zoubi Ala'M, Ibrahim Aljarah, and Hossam Faris, "An Efficient Hybrid Filter and Evolutionary Wrapper Approach for Sentiment Analysis of Various Topics on Twitter," Knowledge-Based Systems, vol. 192, pp. 105353, 2020.
[23] Vashishtha, Srishti, and Seba Susan, "Fuzzy Rule-Based Unsupervised Sentiment Analysis from Social Media Posts," Expert Systems with Applications, vol. 138, pp. 112834, 2019.
[24] Kumar, Akshi, Kathiravan Srinivasan, Wen-Huang Cheng, and Albert Y. Zomaya, "Hybrid Context Enriched Deep Learning Model for Fine-Grained Sentiment Analysis in Textual and Visual Semiotic Modality Social Data," Information Processing & Management, vol. 57, no. 1, pp. 102141, 2020.
[25] Himanshu Thakur, Aman Kumar Sharma, "Supervised Machine Learning Classifiers: Computation of Best Result of Classification Accuracy," International Journal of Computer Trends and Technology, vol. 68, no. 10, pp. 1-8, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I10P101
[26] Jianqiang Z, Xiaolin G, Xuejun Z, “Deep Convolution Neural Networks for Twitter Sentiment Analysis,” IEEE Access, vol. 6, pp. 23253-23260, 2018.
[27] Saif H, Fernandez M, He Y, et al., “SentiCircles for Contextual and Conceptual Semantic Sentiment Analysis of Twitter,” Anissaras, Greece: Springer International Publishing, Cham, Switzerland, pp. 83-98, 2014.
[28] Go A, Bhayani R, Huang L, “Twitter Sentiment Classification using Distant Supervision,” CS224N Project Report, Stanford, pp.12, 2009.
[29] Saif H, He Y, Fernandez M, et al., “Contextual Semantics for Sentiment Analysis of Twitter,” Information Processing and Management, vol. 52, pp.5-19, 2016.
[30] Saif, Hassan, Yulan He, Miriam Fernandez, and Harith Alani, "Adapting Sentiment Lexicons using Contextual Semantics for Sentiment Analysis of Twitter," In European Semantic Web Conference, Springer, Cham, pp. 54-63, 2014.
[31] Michael Speriosu, Nikita Sudan, Sid Upadhyay, and Jason Baldridge, “Twitter Polarity Classification with Label Propagation over Lexical Linksand the Follower Graph,” In Proceedings of the First Workshop on Unsupervised Learning in NLP, EMNLP, pp. 53– 63, 2011.
[32] Santos C. N. d., & Gatti M, “Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts,: In the 25th International Conference on Computational Linguistics, Dublin, Ireland, vol. 2014, pp. 69-78, 2014.
[33] "Your Home for Data Science," 2016. [Online]. Available: http://www.kaggle.com. Accessed: November 2, 2016.
[34] "API - Sentiment140 - A Twitter Sentiment Analysis Tool," 2016. [Online]. Available: http://help.sentiment140.com/api. Accessed: December 20, 2016.
[35] Koteswararao Y. V, & Rao C. R, “Multichannel Speech Separation using Hybrid GOMF and Enthalpy-Based Deep Neural Networks,” Multimedia Systems, pp. 1-16.
[36] Preethi S, & Aishwarya P, “Combining Wavelet Texture Features and Deep Neural Network for Tumor Detection and Segmentation Over MRI,” Journal of Intelligent Systems, vol. 28, no. 4, pp. 571-588, 2019.
[37] Asif Ansari, Sreenarayanan NM, "Analysis of Text Classification of Dataset Using NB-Classifier," SSRG International Journal of Computer Science and Engineering, vol. 7, no. 6, pp. 24-28, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I6P107
[38] Liu, Yang, Jian-Wu Bi, and Zhi-Ping Fan, "Multi-Class Sentiment Classification: The Experimental Comparisons of Feature Selection and Machine Learning Algorithms," Expert Systems with Applications, vol. 80, pp. 323-339, 2017.
[39] Ye, Xin, Hongxia Dai, Luan Dong, and Xinyue Wang, "Multi-View Ensemble Learning Method for Microblog Sentiment Classification," Expert Systems with Applications, pp. 113987, 2020.