[1] |
Jain, Tejashri Inadarchand and Nemade, Dipak. Recognizing contextual polarity in phrase-level sentiment analysis. International Journal of Computer Applications. 7(5): 12--21, Citeseer. 2010. [DOI ] |
[2] |
Liu, Bing. Sentiment analysis and opinion mining. Springer Nature. 2022. [DOI ] |
[3] |
Hagenau, Michael and Liebmann, Michael and Neumann, Dirk. Automated news reading: Stock price prediction based on financial news using context-capturing features. Decision support systems. 55(3): 685--697, Elsevier. 2013. [DOI ] |
[4] |
Dinu, Georgiana and Thater, Stefan. Saarland: Vector-based models of semantic textual similarity. * SEM 2012: The First Joint Conference on Lexical and Computational Semantics--Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012). 603--607, 2012. [DOI ] |
[5] |
Hu, Minqing and Liu, Bing. Mining and summarizing customer reviews. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. 168--177, 2004. [DOI ] |
[6] |
Qiu, Guang and He, Xiaofei and Zhang, Feng and Shi, Yuan and Bu, Jiajun and Chen, Chun. DASA: dissatisfaction-oriented advertising based on sentiment analysis. Expert Systems with Applications. 37(9): 6182--6191, Elsevier. 2010. [DOI ] |
[7] |
Khodaei A, Bastanfard A. Studying and classification of labeled text corpus in Persian. 4th National Conference on Information Technology, Computer and Telecommunication. 2017. [DOI ] |
[8] |
Asgarian, Ehsan and Kahani, Mohsen and Sharifi, Shahla. Hesnegar: Persian sentiment wordnet. Signal and Data Processing. 15(1): 71--86, Signal and Data Processing. 2018. [DOI ] |
[9] |
Hosseini, Pedram and Ramaki, Ali Ahmadian and Maleki, Hassan and Anvari, Mansoureh and Mirroshandel, Seyed Abolghasem. SentiPers: a sentiment analysis corpus for Persian. arXiv preprint arXiv:1801.07737. 2018. [DOI ] |
[10] |
Tavakoli A, Garmaseh V. Provide a Method to analyze emotions in the text of comments. 1th National Conference on Interdisciplinary Research in Computer Engineering, Electrical, Mechanical and Mechatronics. , 2017. [DOI ] |
[11] |
Najafzadeh, Mohsen and Rahati Quchan, Saeed and Ghaemi, Reza. A semi-supervised framework based on self-constructed adaptive lexicon for Persian sentiment analysis. Signal and Data Processing. 15(2): 89--102, Signal and Data Processing. 2018. [DOI ] |
[12] |
Tsytsarau, Mikalai and Palpanas, Themis. Survey on mining subjective data on the web. Data Mining and Knowledge Discovery. 24: 478--514, Springer. 2012. [DOI ] |
[13] |
Hung, Chihli and Lin, Hao-Kai. Using objective words in SentiWordNet to improve word-of-mouth sentiment classification. IEEE Intelligent Systems. 28(02): 47--54, IEEE Computer Society. 2013. [DOI ] |
[14] |
Goeuriot, Lorraine and Na, Jin-Cheon and Min Kyaing, Wai Yan and Khoo, Christopher and Chang, Yun-Ke and Theng, Yin-Leng and Kim, Jung-Jae. Sentiment lexicons for health-related opinion mining. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium. 219--226, 2012. [DOI ] |
[15] |
Huang, Sheng and Niu, Zhendong and Shi, Chongyang. Automatic construction of domain-specific sentiment lexicon based on constrained label propagation. Knowledge-Based Systems. 56: 191--200, Elsevier. 2014. [DOI ] |
[16] |
Hatzivassiloglou, V. Predicting the Semantic Orientation of Adjectives. Proceedings of the 8th conference on European chapter of the Association for Computational Linguistics. 1997. [DOI ] |
[17] |
Fahrni, Angela and Klenner, Manfred. Old wine or warm beer: Target-specific sentiment analysis of adjectives. University of Zurich. 2008. [DOI ] |
[18] |
Kim, Soo-Min and Hovy, Eduard. Determining the sentiment of opinions. Coling 2004: Proceedings of the 20th international conference on computational linguistics. 1367--1373, 2004. [DOI ] |
[19] |
Zhang, Wenhao and Xu, Hua and Wan, Wei. Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis. Expert Systems with Applications. 39(11): 10283--10291, Elsevier. 2012. [DOI ] |
[20] |
Strapparava, Carlo and Mihalcea, Rada. Semeval-2007 task 14: Affective text. Proceedings of the fourth international workshop on semantic evaluations (SemEval-2007). 70--74, 2007. [DOI ] |
[21] |
Danisman, Taner and Alpkocak, Adil. Feeler: Emotion classification of text using vector space model. AISB 2008 convention communication, interaction and social intelligence. 1(4): 53--59, 2008. |
[22] |
Miller, George A. WordNet: a lexical database for English. Communications of the ACM. 38(11): 39--41, ACM New York, NY, USA. 1995. [DOI ] |
[23] |
Cambria, Erik and Speer, Robert and Havasi, Catherine and Hussain, Amir. Senticnet: A publicly available semantic resource for opinion mining. 2010 AAAI fall symposium series. 2010. [DOI ] |
[24] |
Neviarouskaya, Alena and Prendinger, Helmut and Ishizuka, Mitsuru. SentiFul: A lexicon for sentiment analysis. IEEE Transactions on Affective Computing. 2(1): 22--36, IEEE. 2011. [DOI ] |
[25] |
Mohammad, Saif and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana. Semeval-2018 task 1: Affect in tweets. Proceedings of the 12th international workshop on semantic evaluation. 1--17, 2018. [DOI ] |
[26] |
Kiritchenko, Svetlana and Mohammad, Saif and Salameh, Mohammad. Semeval-2016 task 7: Determining sentiment intensity of english and arabic phrases. Proceedings of the 10th international workshop on semantic evaluation (SEMEVAL-2016). 42--51, 2016. [DOI ] |
[27] |
Dehdarbehbahani, Iman and Shakery, Azadeh and Faili, Heshaam. Semi-supervised word polarity identification in resource-lean languages. Neural networks. 58: 50--59, Elsevier. 2014. [DOI ] |
[28] |
Sabeti, Behnam and Hosseini, Pedram and Ghassem-Sani, Gholamreza and Mirroshandel, Seyed Abolghasem. LexiPers: An ontology based sentiment lexicon for Persian. arXiv preprint arXiv:1911.05263. 2019. [DOI ] |
[29] |
Fellbaum, Christiane. WordNet. Theory and applications of ontology: computer applications. 231--243, Springer. 2010. [DOI ] |
[30] |
Roshanfekr, Behnam and Khadivi, Shahram and Rahmati, Mohammad. Sentiment analysis using deep learning on Persian texts. 2017 Iranian conference on electrical engineering (ICEE). 1503--1508, 2017. [DOI ] |
[31] |
Asgarian, Ehsan and Kahani, Mohsen and Sharifi, Shahla. The impact of sentiment features on the sentiment polarity classification in Persian reviews. Cognitive Computation. 10: 117--135, Springer. 2018. [DOI ] |
[32] |
Mohammad, Saif M and Kiritchenko, Svetlana. Using hashtags to capture fine emotion categories from tweets. Computational Intelligence. 31(2): 301--326, Wiley Online Library. 2015. [DOI ] |
[33] |
Ortony, Andrew and Clore, Gerald L and Collins, Allan. The cognitive structure of emotions. Cambridge university press. 2022. [DOI ] |
[34] |
Wiebe, Janyce and Wilson, Theresa and Cardie, Claire. Annotating expressions of opinions and emotions in language. Language resources and evaluation. 39: 165--210, Springer. 2005. [DOI ] |
[35] |
Wilson, T. Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. Proceedings of HLT/EMNLP. 2005. [DOI ] |
[36] |
Somasundaran, Swapna and Wiebe, Janyce and Ruppenhofer, Josef. Discourse level opinion interpretation. Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008). 801--808, 2008. [DOI ] |
[37] |
Hu, Minqing and Liu, Bing. Mining opinion features in customer reviews. AAAI. 4(4): 755--760, 2004. |
[38] |
Pang, Bo and Lee, Lillian. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. arXiv preprint cs/0506075. 2005. [DOI ] |
[39] |
Ekman, Paul. Emotional and conversational nonverbal signals. Language, knowledge, and representation: Proceedings of the sixth international colloquium on cognitive science (ICCS-99). 39--50, 2004. [DOI ] |
[40] |
Perikos, Isidoros and Hatzilygeroudis, Ioannis. Recognizing emotions in text using ensemble of classifiers. Engineering Applications of Artificial Intelligence. 51: 191--201, Elsevier. 2016. [DOI ] |
[41] |
Hu, Kai and Wu, Huayi and Qi, Kunlun and Yu, Jingmin and Yang, Siluo and Yu, Tianxing and Zheng, Jie and Liu, Bo. A domain keyword analysis approach extending Term Frequency-Keyword Active Index with Google Word2Vec model. Scientometrics. 114: 1031--1068, Springer. 2018. [DOI ] |
[42] |
Graham, Yvette and Baldwin, Timothy and Mathur, Nitika. Accurate evaluation of segment-level machine translation metrics. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1183--1191, 2015. [DOI ] |
[43] |
Chen, Guo and Xiao, Lu. Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods. Journal of Informetrics. 10(1): 212--223, Elsevier. 2016. [DOI ] |
[44] |
Webb, Noreen M and Shavelson, Richard J and Haertel, Edward H. 4 reliability coefficients and generalizability theory. Handbook of statistics. 26: 81--124, Elsevier. 2006. [DOI ] |
[45] |
Yadav, Ashima and Vishwakarma, Dinesh Kumar. Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review. 53(6): 4335--4385, Springer. 2020. [DOI ] |
[46] |
Mikolov, T. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. 2013. [DOI ] |
[47] |
Srivastava, Nitish and Hinton, Geoffrey and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research. 15(1): 1929--1958, JMLR. org. 2014. |
[48] |
Medhat, Walaa and Hassan, Ahmed and Korashy, Hoda. Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal. 5(4): 1093--1113, Elsevier. 2014. [DOI ] |
[49] |
Dashtipour, Kia and Hussain, Amir and Zhou, Qiang and Gelbukh, Alexander and Hawalah, Ahmad YA and Cambria, Erik. PerSent: A freely available Persian sentiment lexicon. Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings 8. 310--320, 2016. [DOI ] |
[50] |
Dehkharghani, Rahim. Sentifars: A persian polarity lexicon for sentiment analysis. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP). 19(2): 1--12, ACM New York, NY, USA. 2019. [DOI ] |
[51] |
Pandey, Avinash Chandra and Rajpoot, Dharmveer Singh and Saraswat, Mukesh. Twitter sentiment analysis using hybrid cuckoo search method. Information Processing \& Management. 53(4): 764--779, Elsevier. 2017. [DOI ] |
[52] |
Cun, YL and Bottou, L and Orr, G and Muller, K. Efficient backprop, neural networks: tricks of the trade. Lecture notes in computer sciences. 1524: 5--50, 1998. [DOI ] |
[53] |
Kingma, Diederik P. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. 2014. [DOI ] |
[54] |
Dozat, Timothy. Incorporating nesterov momentum into adam. 2016. |
[55] |
Lin, Min and Chen, Qiang and Yan, Shuicheng. Network in network. arXiv preprint arXiv:1312.4400. 2013. [DOI ] |
[56] |
Ridnik, Tal and Lawen, Hussam and Noy, Asaf and Ben Baruch, Emanuel and Sharir, Gilad and Friedman, Itamar. Tresnet: High performance gpu-dedicated architecture. proceedings of the IEEE/CVF winter conference on applications of computer vision. 1400--1409, 2021. [DOI ] |
[57] |
Rohani, Anahid Rais and others. Algorithm for persian text sentiment analysis in correspondences on an e-learning social website. Journal of Research in Science, Engineering and Technology. 4(01): 11--15, 2016. [DOI ] |
[58] |
Savargiv, Mohammad and Bastanfard, Azam. Text material design for fuzzy emotional speech corpus based on persian semantic and structure. 2013 International conference on fuzzy theory and its applications (iFUZZY). 380--384, 2013. [DOI ] |
[59] |
Veisi, Hadi and Ghoreishi, Sayed Akbar and Bastanfard, Azam. Spoken term detection for Persian news of Islamic Republic of Iran broadcasting. Signal and Data Processing. 17(4): 67--88, Signal and Data Processing. 2021. [DOI ] |
[60] |
Minoofam, Seyyed Amir Hadi and Bastanfard, Azam and Keyvanpour, Mohammad Reza. TRCLA: a transfer learning approach to reduce negative transfer for cellular learning automata. IEEE transactions on neural networks and learning systems. 34(5): 2480--2489, IEEE. 2021. [DOI ] |
[61] |
E Asgaifar, A Bastanfard. Multilingual Idea Plagiarism Detection Robot for Scientific Text Based on Word Net. 2nd International Conference on Computer Engineering and Information Technology. 34(5): 2107. [DOI ] |
[62] |
Savargiv, Mohammad and Bastanfard, Azam. Persian speech emotion recognition. 2015 7th conference on information and knowledge technology (IKT). 1--5, 2015. [DOI ] |
[63] |
Shi, Wei and Wang, Hongwei and He, Shaoyi. EOSentiMiner: an opinion-aware system based on emotion ontology for sentiment analysis of Chinese online reviews. Journal of Experimental \& Theoretical Artificial Intelligence. 27(4): 423--448, Taylor \& Francis. 2015. [DOI ] |
[64] |
Chatterjee, Ankush and Gupta, Umang and Chinnakotla, Manoj Kumar and Srikanth, Radhakrishnan and Galley, Michel and Agrawal, Puneet. Understanding emotions in text using deep learning and big data. Computers in Human Behavior. 93: 309--317, Elsevier. 2019. [DOI ] |
[65] |
Xu, Dongliang and Tian, Zhihong and Lai, Rufeng and Kong, Xiangtao and Tan, Zhiyuan and Shi, Wei. Deep learning based emotion analysis of microblog texts. Information Fusion. 64: 1--11, Elsevier. 2020. [DOI ] |
[66] |
Nezhad, Zahra Bokaee and Deihimi, Mohammad Ali. A combined deep learning model for Persian sentiment analysis. IIUM Engineering Journal. 20(1): 129--139, 2019. [DOI ] |
[67] |
Polignano, Marco and Basile, Pierpaolo and de Gemmis, Marco and Semeraro, Giovanni. A comparison of word-embeddings in emotion detection from text using bilstm, cnn and self-attention. Adjunct publication of the 27th conference on user modeling, adaptation and personalization. 63--68, 2019. [DOI ] |
[68] |
Ragheb, Waleed and Aze, Jerome and Bringay, Sandra and Servajean, Maximilien. Attention-based modeling for emotion detection and classification in textual conversations. arXiv preprint arXiv:1906.07020. 2019. [DOI ] |
[69] |
Dastgheib, Mohammad Bagher and Koleini, Sara and Rasti, Farzad. The application of deep learning in persian documents sentiment analysis. International Journal of Information Science and Management (IJISM). 18(1): 1--15, Islamic World Science \& Technology Monitoring and Citation Institute (ISC). 2020. [DOI ] |
[70] |
Li, Mingzheng and Chen, Lei and Zhao, Jing and Li, Qiang. Sentiment analysis of Chinese stock reviews based on BERT model. Applied Intelligence. 51: 5016--5024, Springer. 2021. [DOI ] |
[71] |
Peng, Sancheng and Cao, Lihong and Zhou, Yongmei and Ouyang, Zhouhao and Yang, Aimin and Li, Xinguang and Jia, Weijia and Yu, Shui. A survey on deep learning for textual emotion analysis in social networks. Digital Communications and Networks. 8(5): 745--762, Elsevier. 2022. [DOI ] |
[72] |
Malik, Arun and Shabaz, Mohammad and Asenso, Evans and others. Machine learning based model for detecting depression during Covid-19 crisis. Scientific African. 20: e01716, Elsevier. 2023. [DOI ] |
[73] |
Kathiravan, P and Saranya, R and Sekar, Sridurga. Sentiment Analysis of COVID-19 Tweets Using TextBlob and Machine Learning Classifiers: An Evaluation to Show How COVID-19 Opinions Is Influencing Psychological Reactions of People’s Behavior in Social Media. Proceedings of International Conference on Data Science and Applications: ICDSA 2022, Volume 2. 89--106, 2023. [DOI ] |
[74] |
Cronbach, Lee J. Response sets and test validity. Educational and psychological measurement. 6(4): 475--494, Sage Publications Sage CA: Los Angeles, CA. 1946. [DOI ] |
[75] |
Piao, Zhixian. Psychological Emotion Analysis System for Special Needs Children Based on Neural Network. Journal of Electronic Research and Application. 7(2): 12--19, 2023. [DOI ] |
[76] |
Suhaimin, Mohd Suhairi Md and Hijazi, Mohd Hanafi Ahmad and Moung, Ervin Gubin and Nohuddin, Puteri Nor Ellyza and Chua, Stephanie and Coenen, Frans. Social media sentiment analysis and opinion mining in public security: Taxonomy, trend analysis, issues and future directions. Journal of King Saud University-Computer and Information Sciences. 101776, Elsevier. 2023. [DOI ] |
[77] |
Flynn, Terry N and Marley, Anthony AJ. Best-worst scaling: theory and methods. Handbook of choice modelling. 178--201, Edward Elgar Publishing. 2014. [DOI ] |
[78] |
Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzman, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin. Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116. 2019. [DOI ] |
[79] |
Seiffert, Chris and Khoshgoftaar, Taghi M and Van Hulse, Jason and Napolitano, Amri. RUSBoost: A hybrid approach to alleviating class imbalance. IEEE transactions on systems, man, and cybernetics-part A: systems and humans. 40(1): 185--197, IEEE. 2009. [DOI ] |
[80] |
Prokhorenkova, Liudmila and Gusev, Gleb and Vorobev, Aleksandr and Dorogush, Anna Veronika and Gulin, Andrey. CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems. 31: 2018. [DOI ] |
[81] |
Ghasemi, Rouzbeh and Ashrafi Asli, Seyed Arad and Momtazi, Saeedeh. Deep Persian sentiment analysis: Cross-lingual training for low-resource languages. Journal of Information Science. 48(4): 449--462, SAGE Publications Sage UK: London, England. 2022. [DOI ] |
[82] |
Priban, Pavel and Smid, Jakub and Steinberger, Josef and Mistera, Adam. A comparative study of cross-lingual sentiment analysis. Expert Systems with Applications. 247: 123247, Elsevier. 2024. [DOI ] |
[83] |
Basiri, Mohammad Ehsan and Kabiri, Arman. Sentence-level sentiment analysis in Persian. 2017 3rd international conference on pattern recognition and image analysis (IPRIA). 84--89, 2017. [DOI ] |
[84] |
An, Sieun and Ji, Li-Jun and Marks, Michael and Zhang, Zhiyong. Two sides of emotion: Exploring positivity and negativity in six basic emotions across cultures. Frontiers in psychology. 8: 610, Frontiers Media SA. 2017. [DOI ] |
[85] |
Turner, Jonathan H. The stratification of emotions: Some preliminary generalizations. Sociological inquiry. 80(2): 168--199, Wiley Online Library. 2010. [DOI ] |
[86] |
Savargiv, Mohammad and Bastanfard, Azam. Real-time speech emotion recognition by minimum number of features. 2016 Artificial Intelligence and Robotics (IRANOPEN). 72--76, 2016. [DOI ] |
[87] |
Khodaei, Azadeh and Bastanfard, Azam and Saboohi, Hadi and Aligholizadeh, Hossein. A Transfer-Based Deep Learning Model for Persian Emotion Classification. Multimedia Tools and Applications. 1--29, Springer. 2024. [DOI ] |
|