- Mattia Atzeni, Amna Dridi, and Diego Reforgiato Recupero. 2018. Using frame-based resources for sentiment analysis within the financial domain. Progress in AI 7, 4 (2018), 273–294. https://doi.org/10.1007/s13748-018-0162-8Google Scholar
- Mattia Atzeni and Diego Reforgiato Recupero. 2020. Multi-domain sentiment analysis with mimicked and polarized word embeddings for human-robot interaction. Future Gener. Comput. Syst. 110 (2020), 984–999. https://doi.org/10.1016/j.future.2019.10.012Google ScholarCross Ref
- Luca Barbaglia, Sergio Consoli, and Sebastiano Manzan. 2021. Exploring the Predictive Power of News and Neural Machine Learning Models for Economic Forecasting. In Mining Data for Financial Applications, Vol. 12591. Springer, Switzerland AG, 135–149.Google Scholar
- Luca Barbaglia, Sergio Consoli, Sebastiano Manzan, Diego Reforgiato Recupero, Michaela Saisana, and Luca Tiozzo Pezzoli. 2021. Data Science Technologies in Economics and Finance: A Gentle Walk-In. In Data Science for Economics and Finance: Methodologies and Applications. Springer Nature, Switzerland AG, 1–17.Google Scholar
- Johan Bollen, Huina Mao, and Xiaojun Zeng. 2011. Twitter mood predicts the stock market. Journal of computational science 2, 1 (2011), 1–8.Google ScholarCross Ref
- Salvatore Carta, Sergio Consoli, Luca Piras, Alessandro Podda, and Diego Reforgiato Recupero. 2020. Dynamic Industry-Specific Lexicon Generation for Stock Market Forecast. In Lecture Notes in Computer Science, Vol. 12565. Springer Nature, Switzerland AG, 162–176.Google Scholar
- Salvatore Carta, Sergio Consoli, Luca Piras, Alessandro Podda, and Diego Reforgiato Recupero. 2021. Explainable Machine Learning Exploiting News and Domain-Specific Lexicon for Stock Market Forecasting. IEEE Access 9(2021), 30193–30205.Google ScholarCross Ref
- Salvatore Carta, Sergio Consoli, Luca Piras, Alessandro Sebastian Podda, and Diego Reforgiato Recupero Recupero. 2021. Event detection in finance using hierarchical clustering algorithms on news and tweets. PeerJ Computer Science 7(2021), e438.Google ScholarCross Ref
- Salvatore Carta, Sergio Consoli, Alessandro Podda, Diego Reforgiato Recupero, and Maria Madalina Stanciu. 2021. Ensembling and Dynamic Asset Selection for Risk-Controlled Statistical Arbitrage. IEEE Access 9(2021), 29942–29959. https://doi.org/10.1109/ACCESS.2021.3059187Google ScholarCross Ref
- Salvatore Carta, Andrea Medda, Alessio Pili, Diego Reforgiato Recupero, and Roberto Saia. 2019. Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data. Future Internet 11, 1 (2019), 5. https://doi.org/10.3390/fi11010005Google ScholarCross Ref
- Sergio Consoli, Diego Reforgiato Recupero, and Milan Petkovic. 2019. Data Science for Healthcare: Methodologies and Applications. Springer, Switzerland AG.Google Scholar
- Sergio Consoli, Diego Reforgiato Recupero, and Michaela Saisana. 2021. Data Science for Economics and Finance: Methodologies and Applications. Springer Nature, Switzerland AG. https://doi.org/10.1007/978-3-030-66891-4Google Scholar
- Sergio Consoli, Luca Tiozzo Pezzoli, and Elisa Tosetti. 2021. Emotions in Macroeconomic News and their Impact on the European Bond Market. Journal of International Money and Finance Volume 118 (2021), 102472.Google ScholarCross Ref
- Sanjiv R Das and Mike Y Chen. 2007. Yahoo! for Amazon: Sentiment extraction from small talk on the web. Management science 53, 9 (2007), 1375–1388.Google Scholar
- Xiao Ding, Yue Zhang, Ting Liu, and Junwen Duan. 2015. Deep learning for event-driven stock prediction. In Twenty-fourth international joint conference on artificial intelligence. Proceedings IJCAI 2015, Buenos Aires, 2327–2333.Google Scholar
- Amna Dridi, Mattia Atzeni, and Diego Reforgiato Recupero. 2019. FineNews: fine-grained semantic sentiment analysis on financial microblogs and news. Int. J. Machine Learning & Cybernetics 10, 8 (2019), 2199–2207. https://doi.org/10.1007/s13042-018-0805-xGoogle ScholarCross Ref
- Amna Dridi and Diego Reforgiato Recupero. 2019. Leveraging semantics for sentiment polarity detection in social media. Int. J. Mach. Learn. Cybern. 10, 8 (2019), 2045–2055. https://doi.org/10.1007/s13042-017-0727-zGoogle ScholarCross Ref
- Ingrid E Fisher, Margaret R Garnsey, and Mark E Hughes. 2016. Natural language processing in accounting, auditing and finance: A synthesis of the literature with a roadmap for future research. Intelligent Systems in Accounting, Finance and Management 23, 3(2016), 157–214.Google ScholarDigital Library
- Sven S Groth and Jan Muntermann. 2011. An intraday market risk management approach based on textual analysis. Decision Support Systems 50, 4 (2011), 680–691.Google ScholarDigital Library
- Michael Hagenau, Michael Liebmann, and Dirk Neumann. 2013. Automated news reading: Stock price prediction based on financial news using context-capturing features. Decision Support Systems 55, 3 (2013), 685–697.Google ScholarCross Ref
- William L Hamilton, Kevin Clark, Jure Leskovec, and Dan Jurafsky. 2016. Inducing domain-specific sentiment lexicons from unlabeled corpora. In Conference on Empirical Methods in Natural Language Processing, Vol. 2016. NIH Public Access, Proceedings EMNLP 2016, Austin, US, 595–605.Google ScholarCross Ref
- Clayton J Hutto and Eric Gilbert. 2014. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Eighth international AAAI conference on weblogs and social media. Proceedings ICWSM 2014, Ann Arbor, US, 216–225.Google Scholar
- J. Korst, V. Pronk, M. Barbieri, and S. Consoli. 2019. Introduction to classification algorithms and their performance analysis using medical examples. In Data Science for Healthcare: Methodologies and Applications. Springer Nature, Switzerland AG, 39–73.Google Scholar
- Victor Lavrenko, Matt Schmill, Dawn Lawrie, Paul Ogilvie, David Jensen, and James Allan. 2000. Language models for financial news recommendation. In Proceedings of the ninth international conference on information and knowledge management. Association for Computing Machinery, New York, US, 389–396.Google ScholarDigital Library
- Andrew W Lo. 2004. The adaptive markets hypothesis. The Journal of Portfolio Management 30, 5 (2004), 15–29.Google ScholarCross Ref
- Hassan H Malik, Vikas S Bhardwaj, and Huascar Fiorletta. 2011. Accurate information extraction for quantitative financial events. In Proceedings of the 20th ACM international conference on information and knowledge management. Association for Computing Machinery, New York, US, 2497–2500.Google ScholarDigital Library
- Burton G Malkiel and Eugene F Fama. 1970. Efficient capital markets: A review of theory and empirical work. The journal of Finance 25, 2 (1970), 383–417.Google ScholarCross Ref
- T. Matsubara, R. Akita, and K. Uehara. 2018. Stock price prediction by deep neural generative model of news articles. IEICE Transactions on Information and Systems E101D, 4(2018), 901–908. https://doi.org/10.1587/transinf.2016IIP0016 cited By 2.Google Scholar
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. Proceedings NIPS 2013, Nevada, US, 3111–3119.Google Scholar
- Antonio Moreno-Ortiz and Javier Fernández-Cruz. 2015. Identifying polarity in financial texts for sentiment analysis: a corpus-based approach. Procedia-Social and Behavioral Sciences 198 (2015), 330–338.Google ScholarCross Ref
- G. Moro, R. Pasolini, G. Domeniconi, A. Pagliarani, and A. Roli. 2019. Prediction and trading of dow jones from twitter: A boosting text mining method with relevant tweets identification. Communications in Computer and Information Science 976 (2019), 26–42.Google ScholarCross Ref
- Michael Nofer and Oliver Hinz. 2015. Using twitter to predict the stock market. Business & Information Systems Engineering 57, 4 (2015), 229–242.Google ScholarCross Ref
- Nuno Oliveira, Paulo Cortez, and Nelson Areal. 2016. Stock market sentiment lexicon acquisition using microblogging data and statistical measures. Decision Support Systems 85 (2016), 62–73.Google ScholarDigital Library
- Diego Reforgiato Recupero, Andrea Nuzzolese, Sergio Consoli, Valentina Presutti, Silvio Peroni, and Misael Mongiovì. 2015. Extracting knowledge from text using SHELDON, a semantic holistic framEwork for LinkeD ONtology data. In WWW 2015 Companion - Proceedings of the 24th International Conference on World Wide Web. ACM, New York, USA, 235–238. https://doi.org/10.1145/2740908.2742842Google ScholarDigital Library
- Jianfeng Si, Arjun Mukherjee, Bing Liu, Qing Li, Huayi Li, and Xiaotie Deng. 2013. Exploiting topic based twitter sentiment for stock prediction. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computing Linguistics, New York, US, 24–29.Google Scholar
- Sahar Sohangir, Nicholas Petty, and Dingding Wang. 2018. Financial sentiment lexicon analysis. In 2018 IEEE 12th International Conference on Semantic Computing (ICSC). IEEE, US, 286–289.Google ScholarCross Ref
- Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, and Manfred Stede. 2011. Lexicon-based methods for sentiment analysis. Computational linguistics 37, 2 (2011), 267–307.Google Scholar
- Paul C Tetlock, Maytal Saar-Tsechansky, and Sofus Macskassy. 2008. More than words: Quantifying language to measure firms’ fundamentals. The Journal of Finance 63, 3 (2008), 1437–1467.Google ScholarCross Ref
- M.R. Vargas, C.E.M. Dos Anjos, G.L.G. Bichara, and A.G. Evsukoff. 2018. Deep Learning for Stock Market Prediction Using Technical Indicators and Financial News Articles. In 2018 International Joint Conference on Neural Networks (IJCNN), Vol. 2018-July. Proceedings IJCNN 2018, IEEE, Rio de Janeiro, Brazil, 8489208.Google Scholar
- Frank Z Xing, Erik Cambria, and Roy E Welsch. 2018. Natural language based financial forecasting: a survey. Artificial Intelligence Review 50, 1 (2018), 49–73.Google ScholarDigital Library
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- A Big Data framework based on Apache Spark for Industry-specific Lexicon Generation for Stock Market Prediction
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