Relevant bibliographies by topics / Sentiment Indicator

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Sentiment Indicator'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 April 2022

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Journal articles on the topic "Sentiment Indicator"

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Silgoner, Maria Antoinette. "The Economic Sentiment Indicator." Journal of Business Cycle Measurement and Analysis 2007, no. 2 (March 10, 2008): 199–215. http://dx.doi.org/10.1787/jbcma-v2007-art11-en.

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Martináková, Radka, and Svatopluk Kapounek. "Economic sentiment indicator and its information capability in the Czech Republic." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 61, no. 7 (2013): 2491–98. http://dx.doi.org/10.11118/actaun201361072491.

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The paper focuses on the indicators of economic agents’ perceptions in the Czech Republic. We assume that these information are provided by economic sentiment indicator surveys based on the Joint Harmonised EU Programme. The aim of this paper is to offer the alternate methodology of qualitative data transformation (balance statistic data) in relation with the macroeconomic quantitative indicators. In the empirical analysis we distinguished between the indicators of confidence in industry, construction, retail and consumer confidence indicator. We found link between the aggregate economic sentiment indicator and economic activity. Especially, aggregate economic sentiment indicator copies the development of the GDP. However, partial indicators does not follow changes in the specific sectors of the economy. We also found that economic agents underestimate the intensity of the economic recession after the year 2007.Finally, we cannot recommend the economic sentiment indicator as the leading indicator of the future economic activity in the Czech Republic. Our methodological contribution is in quantifying of the consumer survey results by standardization.
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Baker, Malcolm, and Jeremy C. Stein. "Market liquidity as a sentiment indicator." Journal of Financial Markets 7, no. 3 (June 2004): 271–99. http://dx.doi.org/10.1016/j.finmar.2003.11.005.

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Huang, Robin, Na Liu, Mary Ann Nicdao, Mary Mikaheal, Tanya Baldacchino, Annabelle Albeos, Kathy Petoumenos, Kamal Sud, and Jinman Kim. "Emotion sharing in remote patient monitoring of patients with chronic kidney disease." Journal of the American Medical Informatics Association 27, no. 2 (October 21, 2019): 185–93. http://dx.doi.org/10.1093/jamia/ocz183.

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Abstract Objective To investigate the relationship between emotion sharing and technically troubled dialysis (TTD) in a remote patient monitoring (RPM) setting. Materials and Methods A custom software system was developed for home hemodialysis patients to use in an RPM setting, with focus on emoticon sharing and sentiment analysis of patients’ text data. We analyzed the outcome of emoticon and sentiment against TTD. Logistic regression was used to assess the relationship between patients’ emotions (emoticon and sentiment) and TTD. Results Usage data were collected from January 1, 2015 to June 1, 2018 from 156 patients that actively used the app system, with a total of 31 159 dialysis sessions recorded. Overall, 122 patients (78%) made use of the emoticon feature while 146 patients (94%) wrote at least 1 or more session notes for sentiment analysis. In total, 4087 (13%) sessions were classified as TTD. In the multivariate model, when compared to sessions with self-reported very happy emoticons, those with sad emoticons showed significantly higher associations to TTD (aOR 4.97; 95% CI 4.13–5.99; P = < .001). Similarly, negative sentiments also revealed significant associations to TTD (aOR 1.56; 95% CI 1.22–2; P = .003) when compared to positive sentiments. Discussion The distribution of emoticons varied greatly when compared to sentiment analysis outcomes due to the differences in the design features. The emoticon feature was generally easier to understand and quicker to input while the sentiment analysis required patients to manually input their personal thoughts. Conclusion Patients on home hemodialysis actively expressed their emotions during RPM. Negative emotions were found to have significant associations with TTD. The use of emoticons and sentimental analysis may be used as a predictive indicator for prolonged TTD.
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Altin, Mehmet, and Muzaffer Uysal. "Economic Sentiment Indicator as a Demand Determinant." Tourism Analysis 19, no. 5 (November 21, 2014): 581–97. http://dx.doi.org/10.3727/108354214x14116690097855.

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Sorić, Petar, Ivana Lolić, and Mirjana Čižmešija. "European economic sentiment indicator: an empirical reappraisal." Quality & Quantity 50, no. 5 (July 26, 2015): 2025–54. http://dx.doi.org/10.1007/s11135-015-0249-2.

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Meire, Matthijs, Kelly Hewett, Michel Ballings, V. Kumar, and Dirk Van den Poel. "The Role of Marketer-Generated Content in Customer Engagement Marketing." Journal of Marketing 83, no. 6 (September 9, 2019): 21–42. http://dx.doi.org/10.1177/0022242919873903.

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Despite the demonstrated importance of customer sentiment in social media for outcomes such as purchase behavior and of firms’ increasing use of customer engagement initiatives, surprisingly few studies have investigated firms’ ability to influence the sentiment of customers’ digital engagement. Many firms track buyers’ offline interactions, design online content to coincide with customers’ experiences, and face varied performance during events, enabling the modification of marketer-generated content to correspond to the event outcomes. This study examines the role of firms’ social media engagement initiatives surrounding customers’ experiential interaction events in influencing the sentiment of customers’ digital engagement. Results indicate that marketers can influence the sentiment of customers’ digital engagement beyond their performance during customers’ interactions, and for unfavorable event outcomes, informational marketer-generated content, more so than emotional content, can enhance customer sentiment. This study also highlights sentiment’s role as a leading indicator for customer lifetime value.
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Liu, Yuan, Yan Shang, Jianming Shi, and Shouyang Wang. "A New Investor Sentiment Indicator Based on Return Decomposition." Journal of Systems Science and Information 4, no. 2 (April 25, 2016): 121–30. http://dx.doi.org/10.21078/jssi-2016-121-10.

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AbstractThis paper extends the DSSW model to accommodate rational arbitrageurs, optimistic investors and pessimistic investors. We model the price impact by using daily data and create a new methodology to calculate the optimistic and the pessimistic. The new sentiment indicator has high correlation with the other traditional ones, and as a proxy variable of individual share or financial market on daily, it could distinguish the optimistic and the pessimistic. In the empirical research, we develop a time-series model and a cross-section model respectively to explore the explanatory power of highly frequent investor sentiment to idiosyncratic volatility and capital asset mispricing. The results show that the new sentiment indicator can explain 21.31% of idiosyncratic volatility to individual share on average, and it has a great explanation of 36% to capital asset mispricing.
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Forss, Thomas, and Peter Sarlin. "News-sentiment networks as a company risk indicator." Journal Of Network Theory In Finance 4, no. 1 (2018): 65–86. http://dx.doi.org/10.21314/jntf.2018.039.

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Čižmešija, Mirjana, and Petar Sorič. "Assessing Croatian GDP Components Via Economic Sentiment Indicator." Economic Research-Ekonomska Istraživanja 23, no. 4 (January 2010): 1–10. http://dx.doi.org/10.1080/1331677x.2010.11517429.

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Dissertations / Theses on the topic "Sentiment Indicator"

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Jaric, Kosta. "Investor sentiment in the Australian market : the implication of closed-end fund discounts as an indicator of sentiment /." Title page, abstract and table of contents only, 2004. http://web4.library.adelaide.edu.au/theses/09C/09cj374.pdf.

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Altin, Mehmet. "Economic Sentiment Indicator as a Demand Determinant in Tourism: A Case of Turkey." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/42577.

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Tourism is one of the fastest growing industries in the world, employing approximately 220 million people and generating over 9.4% of the world's GDP. The growing contribution of tourism is accompanied by an increased interest in understanding the major factors which influence visitation levels to those countries. Therefore, finding the right variables to understand and estimate tourism demand becomes very important and challenging in policy formulations. The purpose of this study is to introduce Economic Sentiment Indicator (ESI) to the field of tourism demand studies. Using ESI in demand analysis, this study will assist in the ability to tap into individuals' hopes and/or worries for the present and future. The study developed a demand model in which the number of tourist arrivals to Turkey from select EU countries is used as the dependent variable. ESI along with more traditional variables such as Interest Rate, Relative Price, and Relative Exchange Rate were brought into the model as the independent demand determinants. The study utilized such econometric models as ARIMA for seasonality adjustment and ARDL Bound test approach to cointegration for the long and short-run elasticities. ESI was statistically significant in 8 countries out of 13, three of those countries had a negative coefficient and five had a positive sign as proposed by the study. The study posits that ESI is a good indicator to gauge and monitor tourism demand and adding the visitors' state of mind into the demand equation could reduce errors and increase variance in arrivals. Policy makers should monitor ESI as it fluctuates over time. Since we do not have direct influence on travelers' demand for tourism, it is imperative that we use indirect approaches such as price adjustment and creating new packages or promotional expenditures in order to influence or induce demand. Using this information generated from the study, government officials and tourism suppliers could adjust their promotional activities and expenditures in origin countries accordingly.
Master of Science
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Noël, Stéphane. "Différences entre les types pensée et sentiment du Myers-Briggs Type Indicator selon l'humeur négative et positive et le stress." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq23733.pdf.

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Spånberg, Erik. "Out of Sample Forecast of Swedish GDP Growth by the Economic Sentiment Indicator in the Euro Area : A Bayesian Approach." Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-256761.

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In this paper, the predictive capabilities of the Economic Sentiment Indicator (ESI), based on business and consumer surveys in the Euro area, are evaluated by out-of-sample forecasts of Swedish GDP growth. A steady state Bayesian VAR-model is applied to quarterly data from 1996 to 2014. The results show that the inclusion of the ESI improves the forecasting performance, both in the point predictive measurement Root Mean Square Errors and in the forecast density sharpness measurement Log Predictive Density Scores. These findings suggest that international confidence indicators may prove useful in forecasting macroeconomic trends for small open economies.
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Kirø, Magnus Løken. "Tweet Sentiment, Sentiment Trend, and a Comparison with Financial Trend Indicators." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-27032.

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Background: As Twitter has become a global microblogging site, it’s influ-ence in the stock market has become noticeable. This makes tweets an interest-ing medium for gathering sentiment. A sentiment that might influence trendsin the stock market.Motivation: If Twitter can be used to predict future prices in the stock mar-ket the casual investor would gain an advantage over the day-trader and themodern trading algorithms.Another interesting aspect is the role of Twitter in sentiment analysis. Andhow Twitters role as a data source influences trends in the stock market.Data and Experiments: Twitter is used as the data source. It provides easyaccess, lots of data, and many possibilities to use available metadata. To findthe sentiment of a tweet we use two methods, counting positive and negativewords(bag of words), and classifiers (SVM and Naive Bayes). We use movingaverage(MA) and average directional index(ADX) as trend indicators. We cal-culate MA and ADX with data from Oslo stock exchange, and we created ourown indicators, based on MA and ADX, using data from Twitter. Then wecompare the graphs.Findings: We explore the usage of lists of words, dictionaries, in sentimentanalysis. And we look at data retrieval from Twitter and the trend we cancreate from it. To a varying degree we get positive results with the dictionaries,while the trend aggregation lacks the finesse and results we hoped for.Conclusion: Sentiment classification of tweets worked with both bag of words,and trained classifiers. We also managed to aggregate a trend based on senti-ment, but we found no correlation between the financial trend indicators andthe sentiment indicators.
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Rojas, Alabarce Matías Nicolás. "Forecasting activity using sentiment indicators : the case of Chile." Tesis, Universidad de Chile, 2016. http://repositorio.uchile.cl/handle/2250/138586.

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Tesis para optar al grado de Magíster en Análisis Económico
In this article we evaluate, in several dimensions, the ability of two sentiment indicators, consumers and business, to forecast year-on-year variation of Chilean activity. We do in-sample and out-of-sample exercises to evaluate predictive capacity. In out-of-sample exercises, when we predict activity using a constant we find that the business confidence indicator (BCI) have the capacity to improve forecasts, the economic perception index (EPI) do not shown predictive capacity in a naïve context. Adding a univariate structure, the results continue to show predictive ability for the BCI, the variable improve forecast for horizons 1 to 12 month ahead, for the EPI the results show predictive ability in medium term forecast, 9 and 12 months ahead. When we use the model proposed by Urrutia and Sanchez (2008) (USM), BCI continues to show predictive ability, but by itself, on average, does not deliver better forecasts that USM. For EPI we find predictive ability in out-of-sample exercises for medium term forecast. The hit rate exercises shows that BCI and EPI correctly predict changes in direction of activity in most horizons. We conclude that business confidence indicator can be use as a leading indicator of Chilean activity. A contribution of this paper is use Clark and West (2007) test in iterative method of forecast.
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Bruno, Magna Maria dos Santos. "Uma anÃlise do sentimento dos empresÃrios brasileiros dos setores de produtos alimentares e metalurgia no perÃodo 2002 â 2012." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=12427.

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nÃo hÃ
A partir de dados mensais dos Ãndices de confianÃa de produtos alimentares e metalurgia da FGV, jà ajustados sazonalmente pela prÃpria instituiÃÃo, o presente trabalho avalia como a instabilidade econÃmica do perÃodo de 2002 a 2012 impactou nesses Ãndices. Foi selecionada uma amostra de 133 observaÃÃes, no referido perÃodo e Ãs duas variÃveis supracitadas foi aplicado um modelo autoregressivo com valor limite endÃgeno objetivando captar mudanÃas de regime na dinÃmica descrita pelos Ãndices de confianÃa jà citados, bem como de descrever o processo estocÃstico descrito pelas variÃveis selecionadas. Os indicadores qualitativos para essas variÃveis foram entÃo calculados e a metodologia permitiu investigar a linearidade e estacionaridade de suas trajetÃrias, produzindo resultados que revelaram as seguintes situaÃÃes para os Ãndices: i) o Ãndice de metalurgia apresentou uma dinÃmica nÃo linear e raiz unitÃria parcial com valor limite endÃgeno estimado de -5,94 pontos percentuais; ii) o Ãndice de produtos alimentares apresentou dinÃmica linear e estacionaridade da sÃrie. Em conjunto, tais constataÃÃes sugerem que a confianÃa do empresariado do seguimento de produtos alimentares, pertencente ao setor de consumo nÃo cÃclico, foi abalada em menor magnitude no perÃodo de instabilidade econÃmica que a confianÃa do empresariado do seguimento de produtos de metalurgia, pertencente ao setor de consumo cÃclico. Intuitivamente conclui-se que atravÃs da estimaÃÃo dos Ãndices que os referidos seguimentos tambÃm sofreram impactos de intensidades diferentes no contexto macroeconÃmico investigado.
From monthly data confidence indexes of food and metallurgy FGV, already seasonally adjusted by the institution itself, this study assesses how the economic instability of the period of 2002 to 2012 impacted these indexes. A sample of 133 observations was selected during that period to these two variables and an autoregressive model was applied with endogenous threshold value aiming to capture regime changes in the dynamics described by the confidence indices mentioned above, as well as describe the stochastic process described by the variables selected. The qualitative indicators for these variables were then calculated and the methodology allowed to investigate the linearity and stationarity of their trajectory, producing results that revealed the following situations for index: i) the index of metallurgy presented a nonlinear dynamic and partial unit root limit endogenous estimated -5.94 percentage points, ii) the index of food presented linear dynamics and stationarity of the series. Together, these findings suggest that the businessman confidence of following food products belonging to the consumer non-cyclic sector was shaken at a lower intensity in the period of economic instability that businessman confidence following the metallurgy products belonging to the sector consumer cyclic. Intuitively it is concluded that by estimating the indices that these segments also suffered impacts of different intensities in the macroeconomic context investigated.
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Frazzoni, Luca. "Modelli di previsione delle serie storiche macroeconomiche." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/6736/.

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Larsson, Rasmus, and Sebastian Haq. "The dynamics of stock market returns and macroeconomic indicators: An ARDL approach with cointegration." Thesis, KTH, Entreprenörskap och Innovation, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189993.

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Macroeconomic indicators are amongst the most important and used tools for investors as they provide an outlook for the economy and thus improve the assessment of investments e.g. for asset allocation. The purpose of this thesis is to investigate the short- and long-run relationship between the US stock market index S&P500 and six selected macroeconomic indicators during different time regimes during 2000-2016. The chosen indicators are Personal spending, Initial jobless claims, M1 Money supply, Building permits, Michigan Consumers Sentiment index and the ISM Manufacturing index as they measure different parts of the economy and are commonly used by investors. We achieve the purpose by using the Autoregressive Distributed Lags model (ARDL) as it has several advantages in relation to comparable time series models. The results show that all indicators except Personal spending are significant in the long-run on the 1-percent level, in at least one time-regime. All indicators have significant results also in the short-run except the Money Supply (M1), depending on which time period that is under investigation. Our conclusion is that our chosen indicators have different characteristics depending on the current dynamics of the stock market, economic state and other related markets. The practical implication for investors is that different indicators are of limited use depending on the current market dynamics and investors must evaluate the underlying premises of the development of the indicator rather than interpreting a specific datapoint.
Makroekonomiska indikatorer är bland de mest viktiga och använda verktygen av investerare eftersom man kan få en överblick av den ekonomiska utvecklingen och således förbättra beslutsunderlaget vid till exempel tillgångsallokering. Syftet med denna avhandling är att undersöka de kort- och långsiktiga förhållandena mellan det amerikanska aktiemarknadsindexet S&P500 och sex utvalda makroekonomiska indikatorer under olika tidsperioder mellan 2000-2016. De valda indikatorerna är Personal spending, Initial jobless claims, M1 Money supply, Building permits, Michigan Consumers Sentiment index och ISM Manufacturing index eftersom de mäter olika delar av ekonomin och används kontinuerligt av investerare. Vi uppnår syftet genom att använda en Autoregressive Distributed Lags (ARDL) modell då den har flertalet fördelar i förhållande till jämförbara tidsseriemodeller. Resultaten visar att alla indikatorer utom Personal spending är signifikant på lång sikt på enprocentsnivån, över olika tidsperioder. Alla indikatorer har även signifikanta resultat på kort sikt förutom M1 Money supply, beroende på vilken tidsperiod som studeras. Vår slutsats är att dem valda indikatorerna har olika egenskaper beroende på den aktuella dynamiken i aktiemarknaden, ekonomin eller andra relaterade marknader. Den praktiska konsekvensen för investerare är att eftersom olika indikatorer är av begränsad användning beroende på den rådande marknadsdynamiken, måste investeraren noggrant utvärdera de underliggande villkoren för utvecklingen av en unik indikator snarare än att endast tolka en unik datapunkt.
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TEIXEIRA, Michele Svaiger. "A rela????o da atividade de emiss??o de a????es com os fatores macroecon??micos e o sentimento do mercado no Brasil." FECAP, 2015. http://tede.fecap.br:8080/jspui/handle/jspui/708.

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This dissertation analyzes the equity issuance activity (IPOs and follow-ons) relation with economics fundamentals and investor sentiment between 2004 and 2013 in the Brazilian market. Measures of the economy such as GDP (Gross Domestic Product), Selic rate, Gross Fixed Capital Formation (FBCF), exchange rate variance and Bovespa Index were associated to IPOs and follow-ons that occurred in the analyzed period summarized quarterly in monetary and quantitative terms. A modest relation between measures of the economy and equity issuance activity was observed, roughly 30%. The Optimism Index (IO) developed by NEFIN and the Consumer Sentiment Index (ICC) calculated by Fecomercio was defined as sentiment measures on the model and they were not statistically significant in the empirical analysis, contradicting studies about investor sentiment relation with the financial market.
Esta disserta????o analisa a rela????o da atividade de emiss??o de a????es (IPOs e follow-ons) com as vari??veis macroecon??micas e com o sentimento do mercado entre os anos de 2004 e 2013 no Brasil. Fatores macroecon??micos como PIB (Produto Interno Bruto), Taxa Selic, Forma????o Bruta de Capital Fixo (FBCF), varia????o cambial e o ??ndice Bovespa foram relacionados aos IPOs e follow-ons ocorridos no per??odo e sumarizados trimestralmente em termos monet??rios e quantitativos. Identificou-se uma modesta rela????o dessas vari??veis com a atividade de emiss??o de a????es em torno de 30%. O ??ndice de Otimismo (IO) desenvolvido pelo NEFIN e o ??ndice de Confian??a do Consumidor (ICC) apurado pela Fecomercio representaram o sentimento de mercado e n??o possu??ram signific??ncia estat??stica na an??lise emp??rica, contrariando estudos acerca da representatividade do sentimento do investidor no mercado financeiro.
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Books on the topic "Sentiment Indicator"

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Baker, Malcolm. Market liquidity as a sentiment indicator. Cambridge, MA: National Bureau of Economic Research, 2002.

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Cofnas, Abe, ed. Sentiment Indicators. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119204398.

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Sentiment in the Forex market: Indicators and strategies to profit from crowd behavior and market extremes. Hoboken, N.J: John Wiley & Sons, 2008.

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Cofnas, Abe. Sentiment indicators: Renko, price break, Kagi, point and figure : what they are and how to use them to trade. New York: Bloomberg Press, 2010.

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Sentiment indicators: Renko, price break, Kagi, point and figure : what they are and how to use them to trade. New York: Bloomberg Press, 2010.

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Tripkovic, Bosko. Common Sentiment. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198808084.003.0003.

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The chapter analyses the metaethical foundations of the argument from common sentiment. This argument holds that moral emotions of the people in a community indicate the solution to moral problems. Drawing on comparative constitutional practice, the chapter contends that the argument from common sentiment consists of two elements: the emotivist element makes moral judgment dependent on moral feelings, and the relativist element ties these feelings to a specific community. The chapter argues that these elements are incompatible and fail to account for the role of reasoning and reflection in moral judgments. The chapter concludes that the argument from common sentiment is inadequate as an exclusive approach to judicial moral judgment.
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Guidelines on Producing Leading, Composite and Sentiment Indicators. UN, 2019. http://dx.doi.org/10.18356/3b565260-en.

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Saettele, Jamie. Sentiment in the Forex Market: Indicators and Strategies to Profit from Crowd Behavior and Market Extremes. Wiley & Sons, Incorporated, John, 2012.

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Saettele, Jamie. Sentiment in the Forex Market: Indicators and Strategies to Profit from Crowd Behavior and Market Extremes. Wiley & Sons, Incorporated, John, 2009.

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Weir, Deborah J. Timing the Market: How To Profit in Stock Market Using The Yield Curve, Market Sentiment, And Cultural Indicators. Penton Overseas, 2006.

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Book chapters on the topic "Sentiment Indicator"

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Kabadjov, Mijail, Alexandra Balahur, and Ester Boldrini. "Sentiment Intensity: Is It a Good Summary Indicator?" In Human Language Technology. Challenges for Computer Science and Linguistics, 203–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20095-3_19.

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Van, Nguyen Duc, Nguyen Ngoc Doanh, Nguyen Trong Khanh, and Nguyen Thi Ngoc Anh. "Hybrid Classifier by Integrating Sentiment and Technical Indicator Classifiers." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 25–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77818-1_3.

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Genina, Alàa, Mariam Gawich, and Abdelfatah Hegazy. "An Approach for Sentiment Analysis and Personality Prediction Using Myers Briggs Type Indicator." In Advances in Intelligent Systems and Computing, 179–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58669-0_16.

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Ioana-Florina, Coita, Cioban Ștefana, and Mare Codruța. "Is Trust a Valid Indicator of Tax Compliance Behaviour? A Study on Taxpayers’ Public Perception Using Sentiment Analysis Tools." In Digitalization and Big Data for Resilience and Economic Intelligence, 99–108. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93286-2_7.

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Arratia, Argimiro, Gustavo Avalos, Alejandra Cabaña, Ariel Duarte-López, and Martí Renedo-Mirambell. "Sentiment Analysis of Financial News: Mechanics and Statistics." In Data Science for Economics and Finance, 195–216. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_9.

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AbstractThis chapter describes the basic mechanics for building a forecasting model that uses as input sentiment indicators derived from textual data. In addition, as we focus our target of predictions on financial time series, we present a set of stylized empirical facts describing the statistical properties of lexicon-based sentiment indicators extracted from news on financial markets. Examples of these modeling methods and statistical hypothesis tests are provided on real data. The general goal is to provide guidelines for financial practitioners for the proper construction and interpretation of their own time-dependent numerical information representing public perception toward companies, stocks’ prices, and financial markets in general.
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Ye, Zi, Weichen Liu, Qiang Qu, Qingshan Jiang, and Yi Pan. "A Cryptocurrency Price Prediction Model Based on Twitter Sentiment Indicators." In Communications in Computer and Information Science, 411–25. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0852-1_32.

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Huang, Pin-Hsuan, Ping-Yu Hsu, Ming-Shien Cheng, Chen-Wan Huang, and Ni Xu. "The Relationship Between Sentiments of Social Media and Economic Indicators." In Mining Intelligence and Knowledge Exploration, 62–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66187-8_7.

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Förschler, Florian, and Simon Alfano. "Reading Between the Lines: The Effect of Language Sentiment on Economic Indicators." In Lecture Notes in Business Information Processing, 89–104. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52764-2_7.

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Borms, Samuel, Kris Boudt, Frederiek Van Holle, and Joeri Willems. "Semi-supervised Text Mining for Monitoring the News About the ESG Performance of Companies." In Data Science for Economics and Finance, 217–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_10.

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AbstractWe present a general monitoring methodology to summarize news about predefined entities and topics into tractable time-varying indices. The approach embeds text mining techniques to transform news data into numerical data, which entails the querying and selection of relevant news articles and the construction of frequency- and sentiment-based indicators. Word embeddings are used to achieve maximally informative news selection and scoring. We apply the methodology from the viewpoint of a sustainable asset manager wanting to actively follow news covering environmental, social, and governance (ESG) aspects. In an empirical analysis, using a Dutch-written news corpus, we create news-based ESG signals for a large list of companies and compare these to scores from an external data provider. We find preliminary evidence of abnormal news dynamics leading up to downward score adjustments and of efficient portfolio screening.
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Wei, Wenqi, and Irem Önder. "An Exploratory Study of Consumers’ Travel-Related Concerns About COVID-19." In Information and Communication Technologies in Tourism 2022, 245–55. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94751-4_22.

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AbstractThis study explores consumers’ travel-related concerns about the COVID-19 pandemic via YouTube comments. Drawing on the risk perception theory and adopting a Markov Chain approach, this study demonstrates the topics that consumers discussed and empirically illustrates perceived risk in the tourism and hospitality industry via sentiment analysis across four sectors: recreation and entertainment, accommodation, transportation, and food and beverages. Results indicate discussion regarding travel-related videos is not only limited to travel-related topics but also includes a broad perspective of social, political, and historical topics. For instance, hotels have a new function as quarantine facilities with effective disease control procedures and social responsibility for public health. Additionally, health, performance, financial, social, and psychological risks are identified. Whereas the presence of travelers is typically regarded as positive, travelers during the crisis are regarded as “irresponsible” and “selfish” individuals who spread the virus and endanger public health. This shift of perception calls for both the industry and academia at large to educate people about the importance of disease control and rebuild travelers’ image and reputation. Recommendations to reduce the perceived risk in each sector are also provided.
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Conference papers on the topic "Sentiment Indicator"

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Slater, Luke T., John A. Williams, Paul N. Schofield, and Georgios V. Gkoutos. "Exploring Sentiment as a Potential Indicator of Bias in Disease Ontologies." In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021. http://dx.doi.org/10.1109/bibm52615.2021.9669329.

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Marcucci, Juri, Giuseppe Bruno, Attilio Mattiocco, Marco Scarnò, and Donatella Sforzini. "The Sentiment Hidden in Italian Texts Through the Lens of A New Dictionary." In CARMA 2018 - 2nd International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/carma2018.2018.8580.

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The aim of this work is to propose a strategy to classify texts (or parts of them) in an ordinal emotional scale to gauge a sentiment indicator in every domain. In particular, we develop a new dictionary for the Italian language which is built using an objective method where the polarities of synonyms and antonyms are accounted for in an iterative process. To build our sentiment indicator negations and intensifiers are also used, thus considering the context in which the single word is written. We apply our new dictionary to extract the sentiment from a set of around 40 issues of the Bank of Italy quarterly Economic Bulletin. Our results show that our strategy is able to correctly identify the sentiment expressed in the Bulletins, which is correlated to the main macroeconomic variables (such as national GDP, investment, consumption or unemployment rate). Our analysis shows that sentiment represents not only an evaluation of the stylistic way in which texts are written, but also a valid synthesis of all the external factors analysed in the same document.
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Al Bashaireh, Rasha, Vian Sabeeh, and Mohammed Zohdy. "Towards a New Indicator for Evaluating Universities Based on Twitter Sentiment Analysis." In 2019 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2019. http://dx.doi.org/10.1109/csci49370.2019.00261.

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Nikumanesh, Esmaeil, and Madjid Fathi. "An indicator for measuring sentiment and polarity: Applied knowledge discovery using online customer reviews." In 2017 IEEE International Conference on Electro Information Technology (EIT). IEEE, 2017. http://dx.doi.org/10.1109/eit.2017.8053408.

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R. Hodeghatta, Umesh, and Sanath V. Haritsa. "Covid-19 Twitter Sentiments Across the United States in August 2020." In International Conference on AI, Machine Learning and Applications (AIMLA 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111305.

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COVID-19 has drastically affected the entire nation. This study involved collecting tweets and analyzing the COVID tweets for August 2020. The aim was to understand whether people have expressed sentiments related to COVID-19 across all the states of the United States and find any correlation between the sentiment tweets and the number of actual cases reported. Around 400000 COVID-19 Twitter data was collected for August 2020 from the primary Twitter database. A simple NLP-based unigram sentiment analyser, a novel approach different from the traditional machine learning approach, was adopted to identify twitter sentiments. The results indicate that tweets related to COVID demonstrate the two types of sentiments, one related to the deaths and the other about the COVID symptoms. Furthermore, the results show that the sentiments for each category vary from State to State. For example, states of New York, California, Texas are higher tweets sentiments regarding expressing death sentiment, and states of New York, California, Nevada, are higher regarding sentiments of expressing COVID-19 symptoms with an accuracy of 83%. As a part of the research, a new sentiment scorecard was created to provide a sentiment score based on the sentiments of the tweets expressed to the actual reported death cases. The sentiment scores for the ‘symptoms’ class are higher for Maryland, New Jersey, and Oregon, whereas sentiment scores for the 'death' class are higher for Virginia, Delaware, and Hawaii. These sentiment scores indicate that the Twitter users of these states are actively tweeting about symptoms and deaths even though the actual reported cases are less in these states. The analysis results also found no or little correlation between the COVID Tweets and the number of COVID death cases reported across all the states.
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Chen, Chung-Chi, Hen-Hsen Huang, and Hsin-Hsi Chen. "Crowd View: Converting Investors' Opinions into Indicators." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/936.

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This paper demonstrates an opinion indicator (OI) generation system, named Crowd View, with which traders can refer to the fine-grained opinions, beyond the market sentiment (bullish/bearish), from crowd investors when trading financial instruments. We collect the real-time textual information from Twitter, and convert it into five kinds of OIs, including the support price level, resistance price level, price target, buy-side cost, and sell-side cost. The OIs for all component stocks in Dow Jones Industrial Average Index (DJI) are provided, and shown with the real-time stock price for comparison and analysis. The information embedding in the OIs and the application scenarios are introduced.
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Oyasor, Jude, Mpho Raborife, and Pravesh Ranchod. "Sentiment Analysis as an Indicator to Evaluate Gender disparity on Sexual Violence Tweets in South Africa." In 2020 International SAUPEC/RobMech/PRASA Conference. IEEE, 2020. http://dx.doi.org/10.1109/saupec/robmech/prasa48453.2020.9040955.

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Barros Filho, Rubens, Jonathas Magalhães, Marlos Silva, Evandro Costa, and Henrique Luna. "2014 FIFA World Cup: An Initial Analysis of Collective Sentiments in Twitter." In IV Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2015. http://dx.doi.org/10.5753/brasnam.2015.6798.

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Social analysis in Twitter has become a very interesting research scenario. Twitter's popularity and the millions of spontaneous interactions between its users turned the social network into a rich data source. A possible use of this data is to measure collective sentiments related to events. In this short paper, we propose an analysis of the content shared in Twitter during the event 2014 FIFA World Cup Brazil TM. The sentiment classifier utilized in this work is the SentiStrength tool. The steps of this works are Composing the corpus, Preprocessing, Classification and Temporal Analysis. Finally, we propose some preliminary results that indicates the distribution of sentiments across the corpus.
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Heiden, Alexandre, and Rafael Stubs Parpinelli. "Applying LSTM for Stock Price Prediction with Sentiment Analysis." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-45.

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Financial news has been proven to be valuable source of information for the evaluation of stock market volatility. Most of the attention has been given to social media platforms, while news from vehicles such as newspapers are not as widely explored. Newspapers provide, although in a smaller volume, more reliable information than social media platforms. In this context, this research aims to examine the influence of financial news within the stock price prediction problem, by using the VADER sentiment analysis model to process the news and feed the sentiments as a feature into a LSTM-based stock price prediction model, along with the historical data of the assets. Experiments indicate that the model has better results when the news’ sentiments are considered, and the model demonstrates potential to accurately predict stock prices up to around 60 days into the future.
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Mušanović, Jelena, Raffaella Folgieri, and Maja Gregorić. "SENTIMENT ANALYSIS AND MULTIMODAL APPROACH APPLIED TO SOCIAL MEDIA CONTENT IN HOSPITALITY INDUSTRY." In Tourism in Southern and Eastern Europe 2021: ToSEE – Smart, Experience, Excellence & ToFEEL – Feelings, Excitement, Education, Leisure. University of Rijeka, Faculty of Tourism and Hospitality Management, 2021. http://dx.doi.org/10.20867/tosee.06.36.

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Purpose – The importance of the "data gold rush" that occurs in real time on various social media platforms is recognized by various tourism stakeholders and researcher. To extract knowledge from textual data, the purpose of this study is to apply text mining techniques to social media data. Methodology – Descriptive statistical analysis is conducted to quantify the activity of hotel brands on Facebook. The topic modelling technique Latent Dirichlet Allocation (LDA) is used to extract and validate knowledge from text data of 25 Croatian four- and five- star hotel brands that were active on social media in 2019. Sentiment analysis is used to identify personal attitudes expressed through user-generated text that hotel brands promote by posting messages on Facebook pages. Findings – The LDA analysis of the Croatian hotel posts extracted 6 topics: Wellbeing, Atmosphere, Promotion, Gastronomy, Surrounding and Satisfaction. The results of the sentiment analysis indicated that Facebook page followers are more likely to express positive sentiments reflecting an overall satisfaction with the promoted products, services and staff by hotel brands. Contribution – It is a unique study that provides an analysis of textual data in Croatian hospitality research. The application of the multimodal approach contributes to a better uses of contents in possible different strategies so that effective indicators can be given to perform an effective communication. This study provides recommendations, challenges, and current insights into applied communication strategies for marketers to increase a greater number of tourists visiting destinations.
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Reports on the topic "Sentiment Indicator"

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Baker, Malcolm, and Jeremy Stein. Market Liquidity as a Sentiment Indicator. Cambridge, MA: National Bureau of Economic Research, February 2002. http://dx.doi.org/10.3386/w8816.

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Salas, Humberto. Medición de la confianza empresarial: un enfoque regional desde la Araucanía. Universidad Autónoma de Chile, July 2020. http://dx.doi.org/10.32457/2050012728/975520193.

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Toloza et al. (2018) describen una dificultad contingente al indicar que en nuestro país no se cuenta con “información robusta” a nivel regional y sectorial para el uso de los distintos agentes económicos. El autor resalta la insuficiente información para la toma de decisiones a nivel territorial y señala que esta es una limitante y un desafío país, por lo que es necesaria la “creación de sistemas regionales integrados de información territorial” (p. 194) para estos fines. El problema anterior, sumado al dilema de comprender el mecanismo bajo el cual estos agentes (empresas, familias y gobierno) forman sus expectativas, representa uno de los desafíos más importantes de la macroeconomía moderna, fundamentalmente en lo que refiere a los ciclos de negocios. Esto, dado que la gran dificultad para estudiar este fenómeno con mayor nivel de desagregación se centra en la disponibilidad de información (Borraz y Gianelli, 2011). Considerando el carácter social que tiene la economía, su comportamiento se ve influenciado por las percepciones de los agentes económicos, respecto de los escenarios futuros. Estas percepciones guardan relación con la coyuntura económica, la política, el resto del mundo y el marco jurídico en un país, entre otros elementos. Es en este contexto en que los gobiernos y el sector privado deben alcanzar un consenso sobre políticas económicas y sociales que produzcan un entorno estable para las regiones (Después de la crisis, 2010). La experiencia internacional indica que una consolidación y un buen manejo de la política fiscal influye fuertemente en el corto plazo en el “sentimiento empresarial” (Michail, et al., 2018). Esta tesis la refuerzan Bachmann y Sims (2012), quienes plantean que una política económica orientada a la consolidación tiene un impacto potente en la economía, a través del canal de confianza empresarial, con efectos que no deben ser subestimados. Considerando que en la Araucanía este componente es importante, la política fiscal juega entonces un papel relevante en las expectativas del empresariado. Es así como en la región, las empresas, gremios, consumidores, comerciantes, agricultores e inversionistas poseen visiones relativas de lo que podría acontecer, considerando su experiencia y apreciaciones, que son muchas veces subjetivas. Se formulan así, período a período, supuestos sobre acontecimientos que buscan explicar escenarios futuros; a este proceso mental colectivo lo llamamos expectativas. No cabe duda de que estas determinan el comportamiento futuro del consumo, del ahorro y la inversión, e incluso de la orientación de la política pública, al impactar directamente en la actividad económica y, más importante, en la generación de empleo e ingresos de la fuerza de trabajo. En consecuencia la situación en la Araucanía no es difiere del problema de cómo los agentes económicos se forman expectativas y cómo logran interactuar con la estructura institucional de la economía, habiendo históricamente interés de los investigadores por incursionar en estas temáticas (Rosser, 2001). La utilización de indicadores que reflejan las expectativas de agentes económicos levantados por medio de encuestas es desarrollada en muchos países que cuentan con un sistema de estadísticas avanzado. Lanzilotta (2014) establece que los indicadores de este tipo son ampliamente utilizados en investigación aplicada, con el fin de capturar y anticipar los movimientos de numerosas variables, para así dar cuenta de la formación de expectativas y los planes de las empresas. El autor plantea que los indicadores de expectativas elaborados a partir tanto de encuestas a empresarios como a consumidores son ampliamente divulgados, principalmente con dos objetivos: explorar los mecanismos de formación de expectativas e identificar su poder predictivo. Es válido, entonces, examinar la forma en que las expectativas locales tienen efectos concretos para la comunidad y los mecanismos en que estas perspectivas afectan realmente el desempeño económico de un territorio. Es en este contexto que según lo expuesto por Salas (2018), la Facultad de Administración y Negocios de la Universidad Autónoma de Chile —con metodología de la Universidad del Desarrollo y el apoyo de la Multigremial de la Araucanía, representante de SOFOFA en la zona— ha aunado esfuerzos para medir la confianza empresarial en la región, desde junio de 2017, creando así el primer Índice de Confianza Empresarial de la Araucanía (ICE Araucanía). Esta iniciativa es relevante, considerando la escasa información para la toma de decisiones del empresariado local en la zona y que la disposición de esta permite a los distintos sectores competir en entornos cambiantes. Así se busca contribuir y hacernos cargo en parte de un problema general que dejó la última crisis económica mundial. Esta manifestó la debilidad de América Latina y de las regiones para competir en una economía global y compleja, debido a la poca información disponible para la toma de decisiones empresariales (Después de la crisis, 2010).
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