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
2095205969 2088272729 2098867768 2171468534 2031567042 2008803468 1977969398 2126181565 2043576616 1500704448 2963631414 359818833 2163772203
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. Summary Alterations in monoaminergic neurotransmission in the brain are thought to underlie seasonal variations in mood, behaviour, and affective disorders. We took blood samples from internal jugular veins in 101 healthy men, to assess the relation between concentration of serotonin metabolite in these samples and weather conditions and season. We showed that turnover of serotonin by the brain was lowest in winter (p=0·013). Moreover, the rate of production of serotonin by the brain was directly related to the prevailing duration of bright sunlight (r=0·294, p=0·010), and rose rapidly with increased luminosity. Our findings are further evidence for the notion that changes in release of serotonin by the brain underlie mood seasonality and seasonal affective disorder. We identified individual-level diurnal and seasonal mood rhythms in cultures across the globe, using data from millions of public Twitter messages. We found that individuals awaken in a good mood that deteriorates as the day progresses—which is consistent with the effects of sleep and circadian rhythm—and that seasonal change in baseline positive affect varies with change in daylength. People are happier on weekends, but the morning peak in positive affect is delayed by 2 hours, which suggests that people awaken later on weekends. Abstract Background: Physical exercise alleviates depressive symptoms, as does exposure to bright light, especially in those with seasonal variation. Our objective was to compare the effect of exercise alone or combined with morning bright light on mood and the health-related quality of life in healthy subjects. Methods: Study subjects were working-age adults, randomized in two groups ( n =80): exercise in bright light (group A), or exercise in normal indoor illumination (group B). Intervention lasted for 8 weeks and questionnaire data on mood and the health-related quality of life were collected at study entry, and at weeks 4 and 8. Results: Physical exercise both in normal indoor illumination and in bright light was effective at alleviating depressive symptoms. The exercise was significantly more effective at alleviating so-called atypical depressive symptoms when combined with bright-light exposure. Limitations: There was no active placebo condition, but a comparative, randomized trial was executed. Conclusions: Physical exercise in bright light had a positive effect on mood and health-related quality of life in a sample of healthy, working-age people. Further research is needed to explore the mechanisms of the apparent additive effect of exercise and light. How do emotions and moods color cognition? In this article, we examine how such reactions influence both judgments and cognitive performance. We argue that many affective influences are due, not to affective reactions themselves, but to the information they carry about value. The specific kind of influence that occurs depends on the focus of the agent at the time. When making evaluative judgments, for example, an agent's positive affect may emerge as a positive attitude toward a person or object. But when an agent focuses on a cognitive task, positive affect may act like feedback about the value of one's approach. As a result, positive affect tends to promote cognitive, relational processes, whereas negative affect tends to inhibit relational processing, resulting in more perceptual, stimulus-specific processing. As a consequence, many textbook phenomena from cognitive psychology occur readily in happy moods, but are inhibited or even absent in sad moods (149). We examined whether press reports on the collective mood of investors can predict changes in stock prices. We collected data on the use of emotion words in newspaper reports on traders' affect, coded these emotion words according to their location on an affective circumplex in terms of pleasantness and activation level, and created indices of collective mood for each trading day. Then, by using time series analyses, we examined whether these mood indices, depicting investors' emotion on a given trading day, could predict the next day's opening price of the stock market. The strongest findings showed that activated pleasant mood predicted increases in NASDAQ prices, while activated unpleasant mood predicted decreases in NASDAQ prices. We conclude that both valence and activation levels of collective mood are important in predicting trend continuation in stock prices. We present an empirically verified model of discernable emotions, Watson and Tellegen’s Circumplex Theory of Affect from social and personality psychology, and suggest its usefulness in NLP as a potential model for an automation of an eight-fold categorization of emotions in written English texts. We developed a data collection tool based on the model, collected 287 responses from 110 non-expert informants based on 50 emotional excerpts (min=12, max=348, average=86 words), and analyzed the inter-coder agreement per category and per strength of ratings per subcategory. The respondents achieved an average 70.7 agreement in the most commonly identified emotion categories per text. The categories of high positive affect and pleasantness were most common in our data. Within those categories, the affective terms “enthusiastic”, “active”, “excited”, “pleased”, and “satisfied” had the most consistent ratings of strength of presence in the texts. The textual clues the respondents chose had comparable length and similar key words. Watson and Tellegen’s model appears to be usable as a guide for development of an NLP algorithm for automated identification of emotion in English texts, and the non-expert informants (with college degree and higher) provided sufficient information for future creation of a gold standard of clues per category. Numerous weather parameters affect the occurrence and amount of rainfall. Therefore, it is important to study these parameters and their interdependency. In this paper, different weather and time-related variables - relative humidity, solar radiation, temperature, dew point, day-of-year, and time-of-day are analyzed systematically using Principal Component Analysis (PCA). We found that four principal components explain a cumulative variance of 85 . The first two principal components are applied to distinguish rain and no-rain scenarios as well. We conclude that all 7 variables have similar contribution towards rainfall detection. We perform a sentiment analysis of all tweets published on the microblogging platform Twitter in the second half of 2008. We use a psychometric instrument to extract six mood states (tension, depression, anger, vigor, fatigue, confusion) from the aggregated Twitter content and compute a six-dimensional mood vector for each day in the timeline. We compare our results to a record of popular events gathered from media and sources. We find that events in the social, political, cultural and economic sphere do have a significant, immediate and highly specific effect on the various dimensions of public mood. We speculate that large scale analyses of mood can provide a solid platform to model collective emotive trends in terms of their predictive value with regards to existing social as well as economic indicators. Statistical and balance features of forecast errors are generally incorporated in the background constraint of variational data assimilation. Forecast error covariances are here estimated with a spectral approach and from a set of forecast differences; autocovariances are calculated with a nonseparable scheme, and multiple linear regressions are used in the formulation of cross covariances. Such an approach was first developed for global models; it is here adapted to ALADIN, a bi-Fourier high-resolution limited-area model, and extended to a multivariate study of humidity forecast errors. Results for autocovariances confirm the importance of nonseparability, in terms of both vertical variability of horizontal correlations and dependence of vertical correlations with horizontal scale; high-resolution spatial correlations are obtained, which should enable a high-resolution analysis. Moreover nonnegligible relationships are found between forecast errors of humidity and those of mass and wind fields.