|
[1] M. Kang, J. Ahn and K. Lee, "Opinion Mining using Ensemble Text Hidden Markov Models for Text Classification," Expert Systems with Applications, pp. 218-227, 2018.
|
|
[2] S. Mokarrami Sefidab, S. A. Mirroshandel, H. Ahmadifar and M. Mokarrami, "Adversarial Attacks on a Text Sentiment Analysis Model," Intelligent Multimedia Processing and Communication Systems, vol. 2, no. 2, pp. 9-16, 2021.
|
|
[3] L. Yue, C. Weitong, L. Xue, Z. Wanli and Y. Minghao, "A survey of sentiment analysis in social media," Knowledge and Information Systems, pp. 617-663, 2019.
|
|
[4] T. P.D., "Thumbs up or thumbs down?: Semantic Orientation Applied to Unsupervised Classification of Reviews," 40th Annual Meeting on Association for Computational Linguistics, pp. 417-424, 2002.
|
|
[5] X. Ding, B. Liu and P. S. Yu, "A Holistic Lexicon-based Approach to Opinion Mining," Proceedings of the International Conference on Web Search and Web Data, pp. 231-240, 2008.
|
|
[6] Z. Karimi and K. Nasiri, "Sentiment Analysis of Digikala Opinions using Adaptive Neuro-Fuzzy Inference System," In Proceeding of 4th International Conference on Soft Computing, pp. 1035-1043, 2021.
|
|
[7] M. S. Sabuj, Z. Afrin and K. M. A. Hasan, "Opinion mining using support vector machine with web based diverse data," International Conference on Pattern Recognition and Machine Intelligence, pp. 673-678, 2017.
|
|
[8] M. R. Saleh, M. Teresa Martín-Valdivia, A. Montejo-Ráez and L. A. Ureña López, "Experiments with SVM to classify opinions in different domains," Expert Systems with Applications, pp. 14799-14804, 2011.
|
|
[9] X. Zhu and A. B. Goldberg, "Introduction to Semi-Supervised Learning," Synthesis lectures on artificial intelligence and machine learning 3, no. 1, pp. 1-130, 2009.
|
|
[10] Z. Karimi and S. Shiry Ghidary, "Semi-Supervised Metric Learning in Stratified Spaces via Intergrating Local Constraints and Information-theoretic non-local Constraints," Neurocomputing 312, pp. 165-176, 2018.
|
|
[11] F. Hassan Khan, U. Qamar and S. Bashir, "A Semi-Supervised Approach to Sentiment Analysis using Revised Sentiment Strength based on SentiWordNet," Knowledge and information Systems, pp. 851-872., 2017.
|
|
[12] D. Anand and D. Naorem, "Semi-Supervised Aspect Based Sentiment Analysis for Movies Using Review Filtering," Procedia Computer Science, pp. 86-93, 2016.
|
|
[13] Y. He and D. Zhou, "Self-training from labeled features for sentiment analysis," Information Processing & Management, pp. 606-616, 2011.
|
|
[14] J. Ortigosa-Hernández, J. Diego Rodríguez, L. Alzate, M. Lucania, I. Inza and J. A. Lozano, "Approaching Sentiment Analysis by using Semi-Supervised Learning of Multi-dimensional Classifiers," Neurocomputing 92, pp. 98-115, 2012.
|
|
[15] M. Najafzadeh, S. Rahati Quchan and R. Ghaemi, "A Semi-Supervised Framework based on Self-constructed Adaptive Lexicon for Persian Sentiment Analysis," Signal and Data Processing, pp. 89-102, 2018.
|
|
[16] E. Asgarian, M. Kahani and S. Sharifi, "Hesnegar: Persian sentiment wordnet," Signal and Data Processing, pp. 71-86, 2018.
|
|
[17] Z. Rajabi and M. Hourali, "Sentiment Analysis Methods in Persian Text: A survey," Signal and Data Processing, pp. 107-132, 2022.
|
|
[18] E. Vaziripour, C. Giraud-Carrier and D. Zappala, "Analyzing the Political Sentiment of Tweets in Farsi," Tenth International AAAI Conference on Web and Social Media, 2016.
|
|
[19] Z. Li, Y. Fan, B. Jiang, T. Lei and W. Liu, "A Survey on Sentiment Analysis and Opinion Mining for Social Multimedia," Multimedia Tools and Applications, pp. 6939-6967, 2019.
|
|
[20] Z. Karimi, "Opinion Mining of Drug Reviews using Support Vector Machine for Multiple Instance Learning," 1st International and 3rd National Conference on Biomathematics, pp. 218-224, 2022.
|
|
[21] A. Bagheri and M. Saraee, "Persian Sentiment Analyzer: A Framework based on a Novel Feature Selection Method," International Journal of Artificial Intelligence, pp. 115-129, 2014.
|
|
[22] M. Shams, A. Shakery and H. Faili, "A Nonparametric LDA-based Induction Method for Sentiment Analysis," Artificial Intelligence and Signal Processing, 2012.
|
|
[23] I. Dehdarbehbahani, A. Shakery and H. Faili, "Semi-Supervised Word Polarity Identification in Resource-lean Languages," Neural networks 58, pp. 50-59, 2014.
|
|
[24] K. Dashtipour, A. Hussain, Q. Zhou, A. Gelbukh, A. Y. A. Hawalah and E. Cambria, "PerSent: A Freely Available Persian Sentiment Lexicon," International Conference on Brain Inspired Cognitive Systems, pp. 310-320, 2016.
|
|
[25] E. Cambria, P. Soujanya, H. Amir and L. Bing, "Computational Intelligence for Affective Computing and Sentiment Analysis [Guest Editorial]," IEEE Computational Intelligence Magazine, pp. 16-17, 2019.
|
|
[26] P. Hosseini, A. Ahmadian Ramaki, M. Anvari, H. Maleki and S. A. Mirroshandel, "SentiPers: A Sentiment Analysis Corpus for Persian," Conference on Computational Linguistics, 2013.
|
|
[27] B. Sabeti, P. Hosseini, G. Ghassem-Sani and S. A. Mirroshandel, "An ontology based sentiment lexicon for Persian," Global Conference on Artificial Intelligence (GCAI), pp. 329-339, 2016.
|
|
[28] M. Moradi, P. Khosravizade and V. Bahram, "Constructing tagged corpora with a web approach as a corpus," the 2th symposium on computational Linguistics, 2012.
|
|
[29] K. Dashtipour, M. Gogate, A. Adeel, H. Larijani and A. Hussain, "Sentiment Analysis of Persian Movie Reviews Using Deep Learning," Entropy, 2021.
|
|
[30] P. F. Brown, P. V. de Souza, R. L. Mercer, V. J. D. Pietra and C. L. Jennifer, "Class-based n-gram Models of Natural Language," Computational linguistics, p. 467–479, 1992.
|
|
[31] L. Gonbadi and N. Ranjbar, "Sentiment Analysis of People’s opinion about Iranian National," Intelligent Multimedia Processing and Communication Systems, vol. 3, no. 4, pp. 51-60, 2023.
|
|
[32] M. B. Dastgheib, S. Koleini and F. Rasti, "The Application of Deep Learning in Persian Documents Sentiment Analysis," International Journal of Information Science and Management (IJISM), pp. 1-15, 2020.
|
|
[33] R. Ghasemi, S. A. Ashrafi Asli and S. Momtazi, "Deep Persian sentiment analysis: Cross-lingual training for low-resource languages," ournal of Information Science 48, pp. 449-462, 2022.
|
|
[34] G. Ansari, C. Saxena, T. Ahmad and M. Doja, "Aspect Term Extraction using Graph-based Semi-Supervised Learning," Procedia Computer Science, vol. 167, pp. 2080-2090, 2020.
|
|
[35] Y. Ren, N. Kaji, N. Yoshinaga and M. Kitsuregawa, "Sentiment Classification in Under-resourced Languages using Graph-based Semi-Supervised Learning Methods," IEICE TRANSACTIONS on Information and Systems, pp. 790-797, 2014.
|
|
[36] T. Yang, L. Hu, C. Shi, H. Ji, X. Li and L. Nie, "HGAT: Heterogeneous Graph Attention Networks for Semi-Supervised Short Text Classification," ACM Transactions on Information Systems (TOIS), pp. 1-29, 2021.
|
|
[37] N. F. F. D. Silva, L. F. Coletta and E. R. Hruschka, "A Survey and Comparative Study of Tweet Sentiment Analysis via Semi-Supervised Learning," ACM Computing Surveys (CSUR), pp. 1-26, 2016.
|
|
[38] Z. Jahanbakhsh-Nagadeh, M.-R. Feizi-Derakhshi and A. Sharifi, "A Semi-Supervised Model for Persian Rumor Verification based on Content Information," Multimed Tools Applications 80, p. 35267–35295, 2021.
|
|
