This paper demonstrates state-of-the-art text sentiment analysis tools while developing a new time-series measure of economic sentiment derived from economic and financial newspaper articles from January 1980 to April 2015. We compare the predictive accuracy of a large set of sentiment analysis models using a sample of articles that have been rated by humans on a positivity/negativity scale. The results highlight the gains from combining existing lexicons and from accounting for negation. We also generate our own sentiment-scoring model, which includes a new lexicon built specifically to capture the sentiment in economic news articles. This model is shown to have better predictive accuracy than existing, “off-the-shelf”, models. Lastly, we provide two applications to the economic research on sentiment. First, we show that daily news sentiment is predictive of movements of survey-based measures of consumer sentiment. Second, motivated by Barsky and Sims (2012), we estimate the impulse responses of macroeconomic variables to sentiment shocks, finding that positive sentiment shocks increase consumption, output, and interest rates and dampen inflation.

The COVID-19 pandemic is causing severe disruptions to daily life and economic activity. Reliable assessments of the economic fallout in this rapidly evolving situation require timely data. Existing sentiment indexes are useful indicators of current and future spending but are only available with a lag or have a short history. A new Daily News Sentiment Index provides a way to measure sentiment in real time from 1980 to today. Compared with survey-based measures of consumer sentiment, this index shows an earlier and more pronounced drop in sentiment in recent weeks.

The COVID-19 pandemic is causing severe disruptions to daily life and economic activity. Reliable assessments of the economic fallout in this rapidly evolving situation require timely data. Existing sentiment indexes are useful indicators of current and future spending but are only available with a lag or have a short history. A new Daily News Sentiment Index provides a way to measure sentiment in real time from 1980 to today. Compared with survey-based measures of consumer sentiment, this index shows an earlier and more pronounced drop in sentiment in recent weeks.

This paper demonstrates state-of-the-art text sentiment analysis tools while developing a new time-series measure of economic sentiment derived from economic and financial newspaper articles from January 1980 to April 2015. We compare the predictive accuracy of a large set of sentiment analysis models using a sample of articles that have been rated by humans on a positivity/negativity scale. The results highlight the gains from combining existing lexicons and from accounting for negation. We also generate our own sentiment-scoring model, which includes a new lexicon built specifically to capture the sentiment in economic news articles. This model is shown to have better predictive accuracy than existing, “off-the-shelf”, models. Lastly, we provide an application to the economic research on sentiment. Motivated by Barsky and Sims (2012), we estimate the impulse responses of macroeconomic variables to sentiment shocks. Our results are consistent with their theoretical and empirical predictions. Positive sentiment shocks increase consumption, output, and interest rates and dampen inflation. ∗We thank Armen Berjikly and the Kanjoya and Ultimate Software staff for generously assisting on the project and providing guidance, comments and suggestions. Lily Huang and Ben Shapiro provided excellent research assistance. The paper benefitted from comments from participants at the Econometric Society summer meetings, APAM meetings, and the Federal Reserve System Applied Microeconomics conference. The views expressed in this paper are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of San Francisco or the Board of Governors of the Federal Reserve System. †Federal Reserve Bank of San Francisco, adam.shapiro@sf.frb.org ‡Stanford University, moritz@cs.stanford.edu §Federal Reserve Bank of San Francisco, daniel.wilson@sf.frb.org

Human emotional states are not independent but rather proceed along systematic paths governed by both internal, cognitive factors and external, social ones. For example, anxiety often transitions to disappointment, which is likely to sink to depression before rising to happiness and relaxation, and these states are conditioned by the states of others in our communities. Modeling these complex dependencies can yield insights into human emotion and support more powerful sentiment technologies. We develop a theory of conditional dependencies between emotional states in which emotions are characterized not only by valence (polarity) and arousal (intensity) but also by the role they play in state transitions and social relationships. We implement this theory using conditional random fields (CRFs) that synthesize textual information with information about previous emotional states and the emotional states of others. To assess the power of affective transitions, we evaluate our model in a collection of 'mood' updates from the Experience Project. To assess the power of social factors, we use a corpus of product reviews from a website in which the community dynamics encourage reviewers to be influenced by each other. In both settings, our models yield improvements of statistical and practical significance over ones that classify each text independently of its emotional or social context.

We propose a computational framework for identifying linguistic aspects of politeness. Our starting point is a new corpus of requests annotated for politeness, which we use to evaluate aspects of politeness theory and to uncover new interactions between politeness markers and context. These findings guide our construction of a classifier with domain-independent lexical and syntactic features operationalizing key components of politeness theory, such as indirection, deference, impersonalization and modality. Our classifier achieves close to human performance and is effective across domains. We use our framework to study the relationship between politeness and social power, showing that polite Wikipedia editors are more likely to achieve high status through elections, but, once elevated, they become less polite. We see a similar negative correlation between politeness and power on Stack Exchange, where users at the top of the reputation scale are less polite than those at the bottom. Finally, we apply our classifier to a preliminary analysis of politeness variation by gender and community.