With the significant growth of social media, individuals and organizations are increasingly using public opinion in these media to make their own decisions. The purpose of sentiment analysis is to automatically extract people’s sentiments from those social networks. Social networks related to financial markets, including stock markets, have recently attracted the attention of many individuals and organizations. people in these networks share their opinions and ideas about each share in the form of a post or tweet. In fact, sentiment analysis in this field is the assessment of people’s attitude towards each share. There are different approaches in sentiment analysis, in this article, a hybrid approach is proposed for sentiment analysis. In this way the feature vector used in machine learning is obtained from a lexicon that is automatically extracted from user’s tweets. This lexicon is made by using stock price information related to user’s opinion. Also, by using the next day’s price information of each share, amendments were suggested to this lexicon. Therefore, the lexicon generated for the feature vector was constructed in three ways, and all three methods reported about an 8% improvement over the baseline method in terms of F-score. The baseline method that is considered for this work, is the Persian version of SentiStrength lexicon which is designed for general purpose.
Pang, B. and Lee, L., "Opinion mining and sentiment analysis", Foundations and Trends in Information Retrieval, Vol. 2, No. 1-2, (2008), 1-135. doi: 10.1561/9781601981516
Liu, B. and Zhang, L., A survey of opinion mining and sentiment analysis, Mining Text Data. 2012, Springer.415-463. doi: 10.1007/978-1-4614-3223-4_13
Chaturvedi, I., Cambria, E., Welsch, R.E. and Herrera, F., "Distinguishing between facts and opinions for sentiment analysis: Survey and challenges", Information Fusion, Vol. 44, (2018), 65-77. doi: 10.1016/j.inffus.2017.12. 006
Taboada, M., Brooke, J., Tofiloski, M., Voll, K. and Stede, M., "Lexicon-based methods for sentiment analysis", Computational Linguistics, Vol. 37, No. 2, (2011), 267-307. doi: 10.1162/COLI_a_00049
Miller, G.A., "Wordnet: An electronic lexical database", MIT press, (1998). doi: 10.7551/mitpress/7287.001.0001
Liu, B., "Sentiment analysis and opinion mining", Synthesis Lectures on Human Language Technologies, Vol. 5, No. 1, (2012), 1-167. doi: 10.1007/978-3-031-02145-9
Li, S., "Sentiment classification using subjective and objective views", International Journal of Computer Applications, Vol. 80, No. 7, (2013). doi: 10.5120/13875-1749
Jo, Y. and Oh, A.H., "Aspect and sentiment unification model for online review analysis", in Proceedings of the fourth ACM International Conference On Web search and Data Mining. (2011), 815-824. doi: 10.1145/1935826 .1935932
Maynard, D. and Funk, A., "Automatic detection of political opinions in tweets", in Extended semantic web conference, Springer. (2011), 88-99. doi: 10.1007/978-3-642-25953-1_8
Dashtipour, K., Hussain, A., Zhou, Q., Gelbukh, A., Hawalah, A.Y. and Cambria, E., "Persent: A freely available persian sentiment lexicon", in International conference on brain inspired cognitive systems, Springer. (2016), 310-320. doi: 10.1007/978-3-319-49685-6_28
Turney, P.D., "Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews", arXiv preprint cs/0212032, (2002).
Rani, S. and Kumar, P., "Deep learning based sentiment analysis using convolution neural network", Arabian Journal for Science and Engineering, Vol. 44, No. 4, (2019), 3305-3314. doi: 10.1007/s13369-018-3500-z
Xu, G., Meng, Y., Qiu, X., Yu, Z. and Wu, X., "Sentiment analysis of comment texts based on bilstm", Ieee Access, Vol. 7, No., (2019), 51522-51532. doi: 10.1109/ACCESS.2019.2909919
Rhanoui, M., Mikram, M., Yousfi, S. and Barzali, S., "A cnn-bilstm model for document-level sentiment analysis", Machine Learning and Knowledge Extraction, Vol. 1, No. 3, (2019), 832-847. doi: 10.3390/make1030048
Ahmad, M., Aftab, S. and Ali, I., "Sentiment analysis of tweets using svm", International Journal of Computer Applications, Vol. 177, No. 5, (2017), 25-29. doi: 10.5120/ijca2017915758
Ahmad, M., Aftab, S., Bashir, M.S., Hameed, N., Ali, I. and Nawaz, Z., "Svm optimization for sentiment analysis", International Journal of Advanced Computer Science and Applications, Vol. 9, No. 4, (2018). doi: 10.14569/IJACSA.2018.090455
Korovkinas, K., Danėnas, P. and Garšva, G., "Svm and k-means hybrid method for textual data sentiment analysis", Baltic Journal of Modern Computing, Vol. 7, No. 1, (2019), 47-60. doi: 10.22364/bjmc.2019.7.1.04
Dey, L., Chakraborty, S., Biswas, A., Bose, B. and Tiwari, S., "Sentiment analysis of review datasets using naive bayes and k-nn classifier", arXiv preprint arXiv:1610.09982, (2016).
Narayanan, V., Arora, I. and Bhatia, A., "Fast and accurate sentiment classification using an enhanced naive bayes model", in International Conference on Intelligent Data Engineering and Automated Learning, Springer. (2013), 194-201. doi: 10.1007/978-3-642-41278-3_24
Hajmohammadi, M.S. and Ibrahim, R., "A svm-based method for sentiment analysis in persian language", in International conference on graphic and image processing (ICGIP 2012), SPIE. Vol. 8768, (2013), 697-701. doi: 10.1117/12.2010940
Saraee, M. and Bagheri, A., "Feature selection methods in persian sentiment analysis", in International conference on application of natural language to information systems, Springer. (2013), 303-308. doi: 10.1007/978-3-642-38824-8_29
Basari, A.S.H., Hussin, B., Ananta, I.G.P. and Zeniarja, J., "Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization", Procedia Engineering, Vol. 53, (2013), 453-462. doi: 10.1016/j.proeng.2013.02.059
Ataei, T.S., Darvishi, K., Javdan, S., Minaei-Bidgoli, B. and Eetemadi, S., "Pars-absa: An aspect-based sentiment analysis dataset for persian", arXiv preprint arXiv:1908.01815, (2019).
Dashtipour, K., Gogate, M., Adeel, A., Ieracitano, C., Larijani, H. and Hussain, A., "Exploiting deep learning for persian sentiment analysis", in International conference on brain inspired cognitive systems, Springer. (2018), 597-604. doi: 10.1007/978-3-030-00563-4_58
Derakhshan, A. and Beigy, H., "Sentiment analysis on stock social media for stock price movement prediction", Engineering Applications of Artificial Intelligence, Vol. 85, (2019), 569-578. doi: 10.1016/j.engappai.2019.07 .002
Li, X., Wu, P. and Wang, W., "Incorporating stock prices and news sentiments for stock market prediction: A case of hong kong", Information Processing & Management, Vol. 57, No. 5, (2020), 102212. doi: 10.1016/j.ipm.2020.102212
Cambria, E., Olsher, D. and Rajagopal, D., "Senticnet 3: A common and common-sense knowledge base for cognition-driven sentiment analysis", in Twenty-eighth AAAI conference on artificial intelligence. (2014). doi: 10.1609/aaai.v28i1.8928
Hu, M. and Liu, B., "Mining and summarizing customer reviews", in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. (2004), 168-177. doi: 10.1145/1014052.1014073
Deng, L. and Wiebe, J., "Mpqa 3.0: An entity/event-level sentiment corpus", in Proceedings of the 2015 conference of the North American chapter of the association for computational linguistics: Human Language Technologies. (2015), 1323-1328. doi: 10.3115/v1/N15-1146
Neviarouskaya, A., Prendinger, H. and Ishizuka, M., "Sentiful: A lexicon for sentiment analysis", IEEE Transactions on Affective Computing, Vol. 2, No. 1, (2011), 22-36. doi: 10.1109/T-AFFC.2011.1
Esuli, A. and Sebastiani, F., "Sentiwordnet: A publicly available lexical resource for opinion mining", in Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), (2006).
Mohammad, S.M. and Turney, P.D., "Crowdsourcing a word–emotion association lexicon", Computational Intelligence, Vol. 29, No. 3, (2013), 436-465. doi: 10.1111/j.1467-8640.2012.00460.x
Basiri, M.E., Naghsh-Nilchi, A.R. and Ghassem-Aghaee, N., "A framework for sentiment analysis in persian", Open Transactions on Information Processing, Vol. 1, No. 3, (2014), 1-14. doi: 10.15764/OTIP.2014.03001
Basiri, M.E. and Kabiri, A., "Sentence-level sentiment analysis in persian", in 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), IEEE. (2017), 84-89. doi: 10.1109/PRIA.2017.7983023
Mohammad, S.M., Kiritchenko, S. and Zhu, X., "Nrc-canada: Building the state-of-the-art in sentiment analysis of tweets", arXiv preprint arXiv:1308.6242, (2013).
Thelwall, M., Buckley, K. and Paltoglou, G., "Sentiment strength detection for the social web", Journal of the American Society for Information Science and Technology, Vol. 63, No. 1, (2012), 163-173. doi: 10.1002/asi.21662
Basiri, M.E. and Kabiri, A., "Translation is not enough: Comparing lexicon-based methods for sentiment analysis in persian", in 2017 International Symposium on Computer Science and Software Engineering Conference (CSSE), IEEE. (2017), 36-41. doi: 10.1109/CSICSSE .2017.8320114
Sabeti, B., Hosseini, P., Ghassem-Sani, G. and Mirroshandel, S.A., "Lexipers: An ontology based sentiment lexicon for persian", arXiv preprint arXiv:1911.05263, (2019).
Amiri, F., Scerri, S. and Khodashahi, M., "Lexicon-based sentiment analysis for persian text, In Proceedings of the International Conference Recent Advances in Natural Language Processing, (2015), 9-16.
Dehkharghani, R., "Sentifars: A persian polarity lexicon for sentiment analysis", ACM Transactions on Asian and Low-Resource Language Information Processing, Vol. 19, No. 2, (2019), 1-12. doi: 10.1145/3345627
Haddi, E., Liu, X. and Shi, Y., "The role of text pre-processing in sentiment analysis", Procedia Computer Science, Vol. 17, (2013), 26-32. doi: 10.1016/j.procs.2013 .05.005
AleAhmad, A., Amiri, H., Darrudi, E., Rahgozar, M. and Oroumchian, F., "Hamshahri: A standard persian text collection", Knowledge-Based Systems, Vol. 22, No. 5, (2009), 382-387. doi: 10.1016/j.knosys.2009.05.002
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D. and Kappas, A., "Sentiment strength detection in short informal text", Journal of the American Society for Information Science and Technology, Vol. 61, No. 12, (2010), 2544-2558. doi: 10.1002/asi.21416
Ahangari, M., & Sebti, A. (2023). A Hybrid Approach to Sentiment Analysis of Iranian Stock Market User’s Opinions. International Journal of Engineering, 36(3), 573-584. doi: 10.5829/ije.2023.36.03c.18
MLA
M. Ahangari; A. Sebti. "A Hybrid Approach to Sentiment Analysis of Iranian Stock Market User’s Opinions". International Journal of Engineering, 36, 3, 2023, 573-584. doi: 10.5829/ije.2023.36.03c.18
HARVARD
Ahangari, M., Sebti, A. (2023). 'A Hybrid Approach to Sentiment Analysis of Iranian Stock Market User’s Opinions', International Journal of Engineering, 36(3), pp. 573-584. doi: 10.5829/ije.2023.36.03c.18
VANCOUVER
Ahangari, M., Sebti, A. A Hybrid Approach to Sentiment Analysis of Iranian Stock Market User’s Opinions. International Journal of Engineering, 2023; 36(3): 573-584. doi: 10.5829/ije.2023.36.03c.18