Abstract
The speeches stated by influential politicians can have a decisive impact on the future of a country. In particular, the economic content of such speeches affects the economy of countries and their financial markets. For this reason, we examine a novel dataset containing the economic content of 951 speeches stated by 45 US Presidents from George Washington (April 1789) to Donald Trump (February 2017). In doing so, we use an economic glossary carried out by means of text mining techniques. The goal of our study is to examine the structure of significant interconnections within a network obtained from the economic content of presidential speeches. In such a network, nodes are represented by talks and links by values of cosine similarity, the latter computed using the occurrences of the economic terms in the speeches. The resulting network displays a peculiar structure made up of a core (i.e. a set of highly central and densely connected nodes) and a periphery (i.e. a set of non-central and sparsely connected nodes). The presence of different economic dictionaries employed by the Presidents characterize the core-periphery structure. The Presidents’ talks belonging to the network’s core share the usage of generic (non-technical) economic locutions like “interest” or “trade”. While the use of more technical and less frequent terms characterizes the periphery (e.g. “yield”). Furthermore, the speeches close in time share a common economic dictionary. These results together with the economics glossary usages during the US periods of boom and crisis provide unique insights on the economic content relationships among Presidents’ speeches.
This is a preview of subscription content, access via your institution.









References
Agarwal, A., Gupta, A., Kumar, A., & Tamilselvam, S. G. (2019). Learning risk culture of banks using news analytics. European Journal of Operational Research, 277, 770–783.
Alfaro, C., Cano-Montero, J., Gómez, J., Moguerza, J. M., & Ortega, F. (2016). A multi-stage method for content classification and opinion mining on weblog comments. Annals of Operations Research, 236, 197–213.
Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 59, 1259–1294.
Bail, C. A. (2016). Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media. Proceedings of the National Academy of Sciences, 113, 11823–11828.
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131, 1593–1636.
Balakrishnan, R., Qiu, X. Y., & Srinivasan, P. (2010). On the predictive ability of narrative disclosures in annual reports. European Journal of Operational Research, 202, 789–801.
Bao, Y., & Datta, A. (2014). Simultaneously discovering and quantifying risk types from textual risk disclosures. Management Science, 60, 1371–1391.
Barrat, A., Barthelemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101, 3747–3752.
Battiston, S., Glattfelder, J. B., Garlaschelli, D., Lillo, F., & Caldarelli, G. (2010). The structure of financial networks. In Network science (pp. 131–163). Springer.
Bernauer, J., & Bräuninger, T. (2009). Intra-party preference heterogeneity and faction membership in the 15th German Bundestag: A computational text analysis of parliamentary speeches. German Politics, 18, 385–402.
Bishop, M. (2009). Essential economics: an A to Z guide (Vol. 22). Hoboken: Wiley.
Blasco, N., Corredor, P., Del Rio, C., & Santamarıa, R. (2005). Bad news and Dow Jones make the Spanish stocks go round. European Journal of Operational Research, 163, 253–275.
Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 10, 10008.
Borgatti, S. P., & Everett, M. G. (2000). Models of core/periphery structures. Social Networks, 21, 375–395.
Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 186.
Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemisitry. Scientometrics, 22, 155–205.
Cannon, B. J., Nakayama, M., Sasaki, D., & Rossiter, A. (2018). Shifting policies in conflict arenas: A cosine similarity and text mining analysis of Turkey’s Syria policy, 2012–2016. Journal of Strategic Security, 11, 1–19.
Carretta, A., Farina, V., Martelli, D., Fiordelisi, F., & Schwizer, P. (2011). The impact of corporate governance press news on stock market returns. European Financial Management, 17, 100–119.
Chae, B. K. (2015). Insights from hashtag #supplychain and Twitter analytics: Considering Twitter and Twitter data for supply chain practice and research. International Journal of Production Economics, 165, 247–259.
Cheng, M., & Jin, X. (2019). What do Airbnb users care about? An analysis of online review comments. International Journal of Hospitality Management, 76, 58–70.
Cinelli, M. (2019). Generalized rich-club ordering in networks. Journal of Complex Networks. https://doi.org/10.1093/comnet/cnz002.
Cinelli, M., Ferraro, G., & Iovanella, A. (2018). Rich-club ordering and the dyadic effect: Two interrelated phenomena. Physica A: Statistical Mechanics and its Applications, 490, 808–818.
Clauset, A., Newman, M. E., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70, 066111.
Clemente, G. P., & Grassi, R. (2018). Directed clustering in weighted networks: A new perspective. Chaos, Solitons & Fractals, 107, 26–38.
Cochran, J. J., Curry, D. J., Radhakrishnan, R., & Pinnell, J. (2014). Political engineering: optimizing a US Presidential candidate’s platform. Annals of Operations Research, 215, 63–87.
Fagiolo, G. (2007). Clustering in complex directed networks. Physical Review E, 76, 026107.
Felici, G. (1995). Talking to Sibilla: An approach to context dependent natural language comprehension. European Journal of Operational Research, 85, 263–281.
Feuerriegel, S., & Gordon, J. (2018). Long-term stock index forecasting based on text mining of regulatory disclosures. Decision Support Systems, 112, 88–97.
Feuerriegel, S., & Gordon, J. (2019). News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions. European Journal of Operational Research, 272, 162–175.
Ficcadenti, V., Cerqueti, R., & Ausloos, M. (2019). A joint text mining-rank size investigation of the rhetoric structures of the US Presidents’ speeches. Expert Systems with Applications, 123, 127–142.
Garcia, D. (2013). Sentiment during recessions. The Journal of Finance, 68, 1267–1300.
Groth, S. S., & Muntermann, J. (2011). An intraday market risk management approach based on textual analysis. Decision Support Systems, 50, 680–691.
Hendershott, T., Livdan, D., & Schürhoff, N. (2015). Are institutions informed about news? Journal of Financial Economics, 117, 249–287.
Huang, K. W., & Li, Z. (2011). A multilabel text classification algorithm for labeling risk factors in SEC form 10-K. ACM Transactions on Management Information Systems (TMIS), 2, 18.
Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2, 193–218.
Ji, Z., Pi, H., Wei, W., Xiong, B., Woźniak, M., & Damasevicius, R. (2019). Recommendation based on review texts and social communities: A hybrid model. IEEE Access, 7, 40416–40427. https://doi.org/10.1109/ACCESS.2019.2897586.
Kahveci, E., & Odabaş, A. (2016). Central banks’ communication strategy and content analysis of monetary policy statements: The case of Fed, ECB and CBRT. Procedia-Social and Behavioral Sciences, 235, 618–629.
Kocheturov, A., Pardalos, P. M., & Karakitsiou, A. (2019). Massive datasets and machine learning for computational biomedicine: Trends and challenges. Annals of Operations Research, 276, 5–34.
Kumar, B. S., & Ravi, V. (2016). A survey of the applications of text mining in financial domain. Knowledge-Based Systems, 114, 128–147.
Laver, M., Benoit, K., & Garry, J. (2003). Extracting policy positions from political texts using words as data. American Political Science Review, 97, 311–331.
Lee, W. Y., Bachtiar, M., Choo, C. C. S., & Lee, C. G. (2019). Comprehensive review of hepatitis B Virus-associated hepatocellular carcinoma research through text mining and big data analytics. Biological Reviews, 94, 353–367.
Li, H., Gupta, A., Zhang, J., & Flor, N. (2018). Who will use augmented reality? An integrated approach based on text analytics and field survey. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2018.10.019.
Light, R. (2014). From words to networks and back: Digital text, computational social science, and the case of presidential inaugural addresses. Social Currents, 1, 111–129.
Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of Finance, 66, 35–65.
Loughran, T., & McDonald, B. (2016). Textual analysis in accounting and finance: A survey. Journal of Accounting Research, 54, 1187–1230.
Maldonado, M., & Sierra, V. (2016). Twitter predicting the 2012 US Presidential Election?: Lessons learned from an unconscious value co-creation platform. Journal of Organizational and End User Computing (JOEUC), 28, 10–30.
Malik, M. M., Abdallah, S., & Ala’raj, M. (2018). Data mining and predictive analytics applications for the delivery of healthcare services: A systematic literature review. Annals of Operations Research, 270, 287–312.
Miller, R. G. (1981). Normal univariate techniques. In Simultaneous statistical inference, chapter 2 (pp. 37–108). Springer.
Miller Center (2019a). Final press conference. Retrieved May 25, 2019 from https://millercenter.org/the-presidency/presidential-speeches/january-12-2009-final-press-conference.
Miller Center (2019b). Proclamation of a State of War with Great Britain. Retrieved May 25, 2019 from https://millercenter.org/the-presidency/presidential-speeches/july-9-1812-proclamation-day-fasting-and-prayer.
Miller Center (2019c). Proclamation of day of fasting and prayer. Retrieved May 25, 2019 from https://millercenter.org/the-presidency/presidential-speeches/july-9-1812-proclamation-day-fasting-and-prayer.
Mishra, N., & Singh, A. (2018). Use of Twitter data for waste minimisation in beef supply chain. Annals of Operations Research, 270, 337–359.
Namaki, A., Shirazi, A. H., Raei, R., & Jafari, G. R. (2011). Network analysis of a financial market based on genuine correlation and threshold method. Physica A: Statistical Mechanics and its Applications, 390, 3835–3841.
Nassirtoussi, A. K., Aghabozorgi, S., Wah, T. Y., & Ngo, D. C. L. (2014). Text mining for market prediction: A systematic review. Expert Systems with Applications, 41, 7653–7670.
Newman, M. E. (2003). Mixing patterns in networks. Physical Review E, 67, 026126.
Newman, M. E. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical Review E, 74, 036104.
Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69, 026113.
Ngai, E. W., Hu, Y., Wong, Y., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50, 559–569.
Noldus, R., & Van Mieghem, P. (2015). Assortativity in complex networks. Journal of Complex Networks, 3, 507.
Oliva, G., Scala, A., Setola, R., & Dell’Olmo, P. (2018). Opinion-based optimal group formation. Omega. https://doi.org/10.1016/j.omega.2018.10.008.
Onnela, J. P., Saramäki, J., Kertész, J., & Kaski, K. (2005). Intensity and coherence of motifs in weighted complex networks. Physical Review E, 71, 065103.
Ooms, J. (2017). hunspell: High-Performance Stemmer, Tokenizer, and Spell Checker. https://CRAN.R-project.org/package=hunspell r package version 2.9.
Opsahl, T., Colizza, V., Panzarasa, P., & Ramasco, J. J. (2008). Prominence and control: The weighted rich-club effect. Physical Review Letters, 101, 168702.
Opsahl, T., & Panzarasa, P. (2009). Clustering in weighted networks. Social Networks, 31, 155–163.
Peruzzi, A., Zollo, F., Quattrociocchi, W., & Scala, A. (2018). How news may affect markets’ complex structure: The case of cambridge analytica. Entropy, 20, 765.
Pons, P., & Latapy, M. (2005). Computing communities in large networks using random walks. In P. Yolum, T. Güngör, F. Gürgen, & C. Özturan (Eds.), Computer and information sciences—ISCIS 2005 (pp. 284–293). Springer.
Price, S. M., Doran, J. S., Peterson, D. R., & Bliss, B. A. (2012). Earnings conference calls and stock returns: The incremental informativeness of textual tone. Journal of Banking & Finance, 36, 992–1011.
Raghavan, U. N., Albert, R., & Kumara, S. (2007). Near linear time algorithm to detect community structures in large-scale networks. Physical Review E, 76, 036106.
Ravisankar, P., Ravi, V., Rao, G. R., & Bose, I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision Support Systems, 50, 491–500.
Rosvall, M., & Bergstrom, C. T. (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105, 1118–1123.
Rule, A., Cointet, J. P., & Bearman, P. S. (2015). Lexical shifts, substantive changes, and continuity in State of the Union discourse, 1790–2014. Proceedings of the National Academy of Sciences, 112, 10837–10844.
Schonhardt-Bailey, C., Yager, E., & Lahlou, S. (2012). Yes, Ronald Reagan’s rhetoric was unique-but statistically, how unique? Presidential Studies Quarterly, 42, 482–513.
Schumaker, R. P., & Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS), 27, 12.
Shaban, T. A., Hexter, L., & Choi, J. D. (2017). Event Analysis on the 2016 U.S. Presidential Election using social media. In International conference on social informatics (pp. 201–217). Springer.
Sudhahar, S., Veltri, G. A., & Cristianini, N. (2015). Automated analysis of the us presidential elections using big data and network analysis. Big Data & Society, 2. https://doi.org/10.1177/2053951715572916.
Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62, 1139–1168.
Tsai, M. F., & Wang, C. J. (2017). On the risk prediction and analysis of soft information in finance reports. European Journal of Operational Research, 257, 243–250.
Tumminello, M., Miccichè, S., Lillo, F., Piilo, J., & Mantegna, R. N. (2011). Statistically validated networks in bipartite complex systems. PLoS ONE, 6, 1–11.
Vargo, C. J., Guo, L., McCombs, M., & Shaw, D. L. (2014). Network issue agendas on Twitter during the 2012 US presidential election. Journal of Communication, 64, 296–316.
Wei, Y. M., Mi, Z. F., & Huang, Z. (2015). Climate policy modeling: an online SCI-E and SSCI based literature review. Omega, 57, 70–84.
Wikipedia contributors (2019a). Glossary of economics—Wikipedia, the free encyclopedia. Retrieved May 25, 2000, from https://en.wikipedia.org/w/index.php?title=Glossary_of_economics&oldid=898838737.
Wikipedia contributors (2019b). List of recessions in the united states—Wikipedia, the free encyclopedia. Retrieved May 25, 2000, from https://en.wikipedia.org/w/index.php?title=List_of_recessions_in_the_United_States&oldid=891896869.
Wu, X., Cao, Y., Xiao, Y., & Guo, J. (2018). Finding of urban rainstorm and waterlogging disasters based on microblogging data and the location-routing problem model of urban emergency logistics. Annals of Operations Research. https://doi.org/10.1007/s10479-018-2904-1.
Yuan, H., Xu, W., Li, Q., & Lau, R. (2018). Topic sentiment mining for sales performance prediction in e-commerce. Annals of Operations Research, 270, 553–576.
Zhou, S., & Mondragón, R. J. (2004). The rich-club phenomenon in the internet topology. IEEE Communications Letters, 8, 180–182.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Cinelli, M., Ficcadenti, V. & Riccioni, J. The interconnectedness of the economic content in the speeches of the US Presidents. Ann Oper Res 299, 593–615 (2021). https://doi.org/10.1007/s10479-019-03372-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-019-03372-2
Keywords
- Glossary of economics
- Text mining
- US Presidents’ speeches
- Network analysis
- Clustering