While it has long been believed in psychology that weather somehow influences human's mood, the debates have been going on for decades about how they are correlated. In this paper, we try to study this long-lasting topic by harnessing a new source of data compared from traditional psychological researches: Twitter. We analyze 2 years' twitter data collected by twitter API which amounts to @math of all postings and try to reveal the correlations between multiple dimensional structure of human mood with meteorological effects. Some of our findings confirm existing hypotheses, while others contradict them. We are hopeful that our approach, along with the new data source, can shed on the long-going debates on weather-mood correlation.
In this section, we briefly go through the related work. Mood Weather Relationship in Psychology and Physiology It has long been believed that there should be some sort of correlation between weather and individual mood state. In psychological literature, researches on this topic can be traced back to early 1950s when Nowis @cite_29 tried to explore the external factors that affect individuals' emotions. While correlational analysis between mood and weather has been conducted in multiple psychological researches @cite_52 @cite_0 @cite_38 @cite_7 @cite_15 @cite_21 @cite_25 , conclusions are amazingly diverse. Low levels of humidity @cite_48 , high levels of sunlight @cite_58 @cite_45 @cite_30 , high barometric pressure @cite_38 and high temperatures @cite_58 @cite_37 have been associated with high mood, but high temperature has been associated with low mood in some other work @cite_38 @cite_7 . Meanwhile, some studies traced no direct correlations between mood and any type of weather variables @cite_52 @cite_25 : Watson and Lee @cite_25 traced the daily mood reports of 498 students in Dallas, Texas and found no significant correlations between mood (measured by self-report using the Positive and Negative Affect Scale) and any of the assessed weather variables, including sunshine, barometric pressure, temperature, and precipitation.
@cite_30 @cite_38 @cite_37 @cite_7 @cite_48 @cite_29 @cite_21 @cite_52 @cite_0 @cite_45 @cite_15 @cite_58 @cite_25
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Correlations were calculated between daily self-evaluated mood reports (4 reports day) of 10 student subjects and 10 meteorological-geophysical variables over a 90-day period. The variables included mean and or change measures of temperature, barometric pressure, relative humidity, sunshine hours, wind speeds and global geomagnetic activity. Multiple regression analyses indicated that the weather matrix used could account for not more than 35 of the mood variance on the day of the evaluation. Lag correlations over the previous week clearly indicated a greater number of significant correlations between mood reports and weather of the previous two days. The mean correlation coefficient was 0.27. In general, "lower moods" were associated with fewer sunshine hours, higher relative humidity, and smaller humidity variations than "higher moods". It was concluded that mood reports can show weak responsivity to antecedent weather fluctuations. Ten mood variables were related to eight weather variables in a multidimensional study. Data were collected from 24 male subjects over 11 consecutive days. The mood variables were as follows: concentration, cooperation, anxiety, potency, aggression, depression, sleepiness, scepticism, control, and optimism. The weather variables included: hours of sunshine, precipitation, temperature, wind direction, wind velocity, humidity, change in barometric pressure and absolute barometric pressure. Humidity, temperature and hours of sunshine had the greatest effect on mood. High levels of humidity lowered scores on concentration while increasing reports of sleepiness. Rising temperatures lowered anxiety and scepticism mood scores. Humidity was the most significant predictor in regression and canonical correlation analysis. Implications for school and office performance are discussed which highlight the importance of humidity as a weather variable. Research has shown that greater degrees of sunshine experienced directly are associated with greater amounts of compliance and tipping. This paper describes two studies that investigated beliefs about sunshine and tipping. The studies were conducted at a casino hotel in Atlantic City; a male server who delivered food and drinks to guests' rooms acted as the confederate. In Study 1, the server reported to guests the actual sky conditions: sunny, partly sunny, cloudy, or rainy. Tip percentages increased linearly from the worst to the best conditions. Study 2 used an experimental design. The server informed guests that the weather was either warm and sunny, cold and sunny, warm and rainy, or cold and rainy. Guests were gullible because their rooms shielded them from the actual conditions. Belief in sunny skies produced greater tip percentages. The temperature belief manipulation had no effect. This research extended previous research by showing that beliefs about weather, in addition to actual weather, can affect behavior. Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large, i.e. can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public's response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. We find an accuracy of 87.6 in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6 . Index Terms—stock market prediction — twitter — mood analysis.