Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 4 ، دوره 12، شماره 1 ، فروردین 2024، صفحه 57-68 1.55 M )
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2023.9611.644 نویسنده
S. Nemati*
تاریخ دریافت : 29 فروردین 1402 ،
تاریخ بازنگری : 12 تیر 1402 ،
تاریخ پذیرش : 18 تیر 1402
چکیده Background and Objectives: Twitter is a microblogging platform for expressing assessments, opinions, and sentiments on different topics and events. While there have been several studies around sentiment analysis of tweets and their popularity in the form of the number of retweets, predicting the sentiment of first-order replies remained a neglected challenge. Predicting the sentiment of tweet replies is helpful for both users and enterprises. In this study, we define a novel problem; given just a tweet's text, the goal is to predict the overall sentiment polarity of its upcoming replies.Methods: To address this problem, we proposed a graph convolutional neural network model that exploits the text's dependencies. The proposed model contains two parallel branches. The first branch extracts the contextual representation of the input tweets. The second branch extracts the structural and semantic information from tweets. Specifically, a Bi-LSTM network and a self-attention layer are used in the first layer for extracting syntactical relations, and an affective knowledge-enhanced dependency tree is used in the second branch for extracting semantic relations. Moreover, a graph convolutional network is used on the top of these branches to learn the joint feature representation. Finally, a retrieval-based attention mechanism is used on the output of the graph convolutional network for learning essential features from the final affective picture of tweets.Results: In the experiments, we only used the original tweets of the RETWEET dataset for training the models and ignored the replies of the tweets in the training process. The results on three versions of the RETWEET dataset showed that the proposed model outperforms the LSTM-based models and similar state-of-the-art graph convolutional network models. Conclusion: The proposed model showed promising results in confirming that by using only the content of a tweet, we can predict the overall sentiment of its replies. Moreover, the results showed that the proposed model achieves similar or comparable results with simpler deep models when trained on a public tweet dataset such as ACL 2014 dataset while outperforming both simple deep models and state-of-the-art graph convolutional deep models when trained on the RETWEET dataset. This shows the proposed model's effectiveness in extracting structural and semantic relations in the tweets.کلیدواژهها
Sentiment Analysis ؛ Deep Leaning ؛ Social Media ؛ Twitter ؛ Graph Convolutional Neural Networks
مراجع
[1] J. C. de Gagne, E. Cho, H. K. Park, J. D. Nam, D. Jung, “A qualitative analysis of nursing students’ tweets during the COVID-19 pandemic,” Nurs. Health Sci., 23(1): 273–278, 2021.
[2] M. T. Rajeh, S. N. Sembawa, A. A. Nassar, S. A. al Hebshi, K. T. Aboalshamat, M. K. Badri, “Social media as a learning tool: Dental students’ perspectives,” J. Dent. Educ., 85(4): 513–520, 2021.
[3] S. Rill, D. Reinel, J. Scheidt, R. V. Zicari, “Politwi: Early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis,” Knowl. Based Syst., 69: 24–33, 2014.
[4] B. Castanho Silva, S. O. Proksch, “Politicians unleashed? Political communication on Twitter and in parliament in Western Europe,” Political Sci. Res. Methods, 10(4): 776–792, 2022.
[5] N. Corsi, D. Dan Nguyen, M. Butaney, S. E. Majdalany, M. P. Corsi, T. Malchow, A. J. Piontkowski, Q. D. Trinh, S. Loeb, F. Abdollah, “Top 100 Urology Influencers on Twitter: Is Social Media Influence Associated with Academic Impact?,” Eur. Urol. Focus, 9(2): 396-402, 2022.
[6] B. Mishra et al., “Use of twitter in Neurology: boon or bane?,” J. Med. Internet Res., 23(5): e25229, 2021.
[7] Y. Wang, “Building relationships with fans: how sports organizations used twitter as a communication tool,” Sport Soc., 24(7): 1055–1069, 2021.
[8] L. Ante, “How Elon Musk’s twitter activity moves cryptocurrency markets,” Technol. Forecast Soc. Change, 186: 122112, 2023.
[9] S. Duz Tan, O. Tas, “Social media sentiment in international stock returns and trading activity,” J. Behav. Finance, 22(2): 221–234, 2021.
[10] E. Kouloumpis, T. Wilson, J. Moore, “Twitter sentiment analysis: The good the bad and the omg!,” in Proc. the international AAAI conference on web and social media, 5(1): 538–541, 2011.
[11] A. Giachanou, F. Crestani, “Like it or not: A survey of twitter sentiment analysis methods,” ACM Comput. Surv. (CSUR), 49(2): 1–41, 2016.
[12] M. E. Basiri, S. Nemati, M. Abdar, S. Asadi, U. R. Acharrya, “A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets,” Knowl. Based Syst., 228: 107242, 2021.
[13] K. Sailunaz, R. Alhajj, “Emotion and sentiment analysis from Twitter text,” J. Comput. Sci., 36: 101003, 2019.
[14] J. T. Yun, U. Pamuksuz, B. R. L. Duff, “Are we who we follow? Computationally analyzing human personality and brand following on Twitter,” Int. J. Advert, 38(5): 776–795, 2019.
[15] M. Hasan, M. A. Orgun, R. Schwitter, “A survey on real-time event detection from the Twitter data stream,” J. Inf. Sci., 44(4): 443–463, 2018.
[16] N. Oliveira, P. Cortez, N. Areal, “The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices,” Expert Syst. Appl., 73: 125–144, 2017.
[17] M. H. Tsai, Y. Wang, M. Kwak, N. Rigole, "A machine learning based strategy for election result prediction," in Proc. International Conference on Computational Science and Computational Intelligence (CSCI): 1408-1410, 2019.
[18] M. E. Basiri, S. Nemati, M. Abdar, E. Cambria, U. R. Acharya, “ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis,” Future Gener. Comput. Syst., 115: 279–294, 2021.
[19] M. Cieliebak, J. Deriu, D. Egger, F. Uzdilli, “A Twitter corpus and benchmark resources for German sentiment analysis,” in Proc. 4th International Workshop on Natural Language Processing for social media: 45–55, 2017.
[20] S. Özsoy, “Use of new media by Turkish fans in sport communication: Facebook and Twitter,” J. Hum. Kinet., 28: 165, 2011.
[21] S. O. Alhumoud, M. I. Altuwaijri, T. M. Albuhairi, W. M. Alohaideb, “Survey on arabic sentiment analysis in twitter,” Int. J. Comput. Inf. Eng., 9(1): 364–368, 2015.
[22] E. S. Tellez, S. Miranda-Jiménez, M. Graff, D. Moctezuma, O. S. Siordia, E. A. Villaseñor, “A case study of Spanish text transformations for twitter sentiment analysis,” Expert Syst. Appl., 81: 457–471, 2017.
[23] L. Rivadeneira, J. B. Yang, M. López-Ibáñez, “Predicting tweet impact using a novel evidential reasoning prediction method,” Expert Syst. Appl., 169, 2021.
[24] I. Daga, A. Gupta, R. Vardhan, P. Mukherjee, “Prediction of likes and retweets using text information retrieval,” Procedia Comput. Sci., 168: 123–128, 2020.
[25] S. Butt, S. Sharma, R. Sharma, G. Sidorov, A. Gelbukh, “What goes on inside rumour and non-rumour tweets and their reactions: A psycholinguistic analyses,” Comput. Human Behav., p. 107345, 2022.
[26] M. Abdar et al., “Energy choices in Alaska: Mining people’s perception and attitudes from geotagged tweets,” Renewable Sustainable Energy Rev., 124: 109781, 2020.
[27] S. T. Arasteh et al., “How will your Tweet be received? Predicting the sentiment polarity of Tweet replies,” in Proc. 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021: 370–373, 2021.
[28] X. Gao, Z. Zheng, Q. Chu, S. Tang, G. Chen, Q. Deng, “Popularity prediction for single Tweet based on heterogeneous bass model,” IEEE Trans. Knowl. Data Eng., 33(5): 2165–2178, 2021.
[29] S. Petrovic, M. Osborne, V. Lavrenko, “Rt to win! predicting message propagation in twitter,” in Proc. the international AAAI conference on web and social media, 5(1): 586–589, 2021.
[30] M. Mahdikhani, “Predicting the popularity of tweets by analyzing public opinion and emotions in different stages of Covid-19 pandemic,” Int. J. Inf. Manage. Data Insights, 2(1): 100053, 2022.
[31] M. Pontiki et al., “SemEval-2016 task 5: Aspect based sentiment analysis,” in ProWorkshop on Semantic Evaluation (SemEval-2016): 19–30, 2016.
[32] S. T. Arasteh, M. Monajem, V. Christlein, P. Heinrich, A. Nicolaou, H. Naderi Boldaji, M. Lotfinia, S. Evert, “How will your Tweet be received? Predicting the sentiment polarity of Tweet replies,” in Proc. 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021: 370–373, 2021.
[33] A. Agarwal, B. Xie, I. Vovsha, O. Rambow, R. J. Passonneau, “Sentiment analysis of twitter data,” in Proc. the workshop on language in social media (LSM 2011): 30–38, 2011.
[34] S. M. Mohammad, S. Kiritchenko, X. D. Zhu, “NRC-Canada: Building the State-of-the-Art in sentiment analysis of Tweets,” in Proc. the seventh international workshop on Semantic Evaluation Exercises (SemEval-2013), 2013.
[35] H. Ali, H. Farman, H. Yar, Z. Khan, S. Habib, A. Ammar, “Deep learning-based election results prediction using Twitter activity,” Soft. Comput., 26(16): 7535–7543, 2022.
[36] L. Hong, O. Dan, B. D. Davison, “Predicting popular messages in Twitter,” in Proc. the 20th international conference companion on World wide web: 57–58, 2011.
[37] Z. Luo, M. Osborne, J. Tang, T. Wang, “Who will retweet me? Finding retweeters in Twitter,” in Proc. the 36th international ACM SIGIR conference on Research and development in information retrieval: 869–872, 2013.
[38] L. Wang, Y. Zhang, J. Yuan, K. Hu, S. Cao, “FEBDNN: fusion embedding-based deep neural network for user retweeting behavior prediction on social networks,” Neural Comput. Appl., 34(16): 13219–13235, 2022.
[39] S. N. Firdaus, C. Ding, A. Sadeghian, “Topic specific emotion detection for retweet prediction,” Int. J. Mach. Learn. Cybern., 10(8): 2071–2083, 2019.
[40] I. N. Lymperopoulos, “RC-Tweet: Modeling and predicting the popularity of tweets through the dynamics of a capacitor,” Expert Syst. Appl., 163: 113785, 2021.
[41] M. D. Garvey, J. Samuel, A. Pelaez, “Would you please like my tweet?! An artificially intelligent, generative probabilistic, and econometric based system design for popularity-driven tweet content generation,” Decis. Support Syst., 144: 113497, 2021.
[42] S. Poria, E. Cambria, A. Gelbukh, “Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis,” Association for Computational Linguistics, 2015.
[43] D. C. Edara, L. P. Vanukuri, V. Sistla, V. K. K. Kolli, “Sentiment analysis and text categorization of cancer medical records with LSTM,” J. Ambient Intell. Hum. Comput., 14: 5309-5325, 2023.
[44] M. E. Basiri, M. Abdar, M. A. Cifci, S. Nemati, U. R. Acharya, “A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques,” Knowl. Based Syst., 198: 105949, 2020.
[45] A. M. Shah, X. Yan, S. A. A. Shah, G. Mamirkulova, “Mining patient opinion to evaluate the service quality in healthcare: a deep-learning approach,” J. Ambient Intell. Hum. Comput., 11: 2925-2942, 2020.
[46] M. Parimala, R. M. Swarna Priya, M. Praveen Kumar Reddy, C. Lal Chowdhary, R. Kumar Poluru, S. Khan, “Spatiotemporal-based sentiment analysis on tweets for risk assessment of event using deep learning approach,” Softw.: Pract. Exper., 51(3): 550-570, 2020.
[47] J. Serrano-Guerrero, M. Bani-Doumi, F. P. Romero, J. A. Olivas, “Understanding what patients think about hospitals: A deep learning approach for detecting emotions in patient opinions,” Artif. Intell. Med., 128: 102298, 2022.
[48] M. E. Basiri, R. S. Chegeni, A. N. Karimvand, S. Nemati, “Bidirectional LSTM deep model for online doctor reviews polarity detection,” in Proc. 2020 6th International Conference on Web Research (ICWR), 2020.
[49] H. A. Shehu et al., “Deep sentiment analysis: a case study on stemmed Turkish twitter data,” IEEE Access, vol. 9, pp. 56836–56854, 2021.
[50] A. B. Nassif, A. Elnagar, I. Shahin, S. Henno, “Deep learning for arabic subjective sentiment analysis: Challenges and research opportunities,” Appl. Soft. Comput., 98: 106836, 2021.
[51] H. Elfaik, E. H. Nfaoui, “Deep bidirectional lstm network learning-based sentiment analysis for arabic text,” J. Intell. Syst., 30(1): 395–412, 2021.
[52] H. Saleh, S. Mostafa, L. A. Gabralla, A. O. Aseeri, S. El-Sappagh, “Enhanced arabic sentiment analysis using a novel stacking ensemble of hybrid and deep learning models,” Appl. Sci., 12(18): 8967, 2022.
[53] K. Dashtipour, M. Gogate, A. Adeel, H. Larijani, A. Hussain, “Sentiment analysis of persian movie reviews using deep learning,” Entropy, 23(5): 596, 2021.
[54] Z. B. Nezhad, M. A. Deihimi, “Twitter sentiment analysis from Iran about COVID 19 vaccine,” Diabetes Metab. Syndr., 16(1): 102367, 2021.
[55] S. Smetanin, M. Komarov, “Deep transfer learning baselines for sentiment analysis in Russian,” Inf. Process Manag., 58(3): 102484, 2021.
[56] J. Devlin, M. W. Chang, K. Lee, K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.
[57] N. Reimers, I. Gurevych, “Alternative weighting schemes for elmo embeddings,” arXiv preprint arXiv:1904.02954, 2019.
[58] J. Pennington, R. Socher, C. D. Manning, “Glove: Global vectors for word representation,” in Proc. the 2014 conference on empirical methods in natural language processing (EMNLP): 1532–1543, 2014.
[59] G. Letarte, F. Paradis, P. Giguère, F. Laviolette, “Importance of self-attention for sentiment analysis,” in Proc. the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP: 267–275, 2018.
[60] M. H. Phan P. O. Ogunbona, “Modelling context and syntactical features for aspect-based sentiment analysis,” in Proc. the 58th Annual Meeting of the Association for Computational Linguistics: 3211–3220, 2020.
[61] Y. Vasiliev, Natural Language Processing with Python and SpaCy: A Practical Introduction. No Starch Press, 2020.
[62] C. Zhang, Q. Li, D. Song, “Aspect-based sentiment classification with aspect-specific graph convolutional networks,” arXiv preprint arXiv:1909.03477, 2019.
[63] E. Cambria, R. Speer, C. Havasi, A. Hussain, “Senticnet: A publicly available semantic resource for opinion mining.,” in Proc. AAAI fall symposium: commonsense knowledge, 10(0), 2010.
[64] B. Liang, H. Su, L. Gui, E. Cambria, R. Xu, “Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks,” Knowl. Based Syst., 235: 107643, 2022.
[65] A. Chatterjee, U. Gupta, M. K. Chinnakotla, R. Srikanth, M. Galley, P. Agrawal, “Understanding emotions in text using deep learning and big data,” Comput. Human Behav., 93: 309–317, 2019.
آمار
تعداد مشاهده مقاله: 365
تعداد دریافت فایل اصل مقاله: 321
