What a Nasty Day

Li, Jiwei; Wang, Xun; Hovy, Eduard

doi:10.1145/2661829.2662090

Cited by 30 publications

(10 citation statements)

References 57 publications

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“…The results of the study are consistent with previous studies that have found associations between weather and sentiment on Twitter [31,32,33]. Despite the observed independent correlations between weather and sentiment, weather explained little of the variance in positive or negative sentiment.…”

Section: Comparisons With Past Literature and Implicationssupporting

confidence: 91%

“…Past studies examining weather and sentiment on Twitter have produced variable results, but most observe one or more associations [31,32,33]. We collected hourly weather data for the top 100 cities using the API from the Open Weather website [38].…”

Section: Weather Datamentioning

confidence: 99%

“…However, the results and conclusions vary from study to study, and these differences may depend on the methods used to aggregate sentiment across sets of tweets or users, differences in the ways the investigators sampled the data, differences in the sentiment analysis algorithms or tools used, or because of challenges associated with validating results against external information. In comparison, studies examining variation in sentiment by geography or weather are relatively rare compared with those that measure temporal variation [30,31,32,33,34]. Studies that report analyses for social interactions on Twitter-tweets that mention, reply to, or quote other users-do not appear to have focused on measuring differences in the sentiment relative to tweets that broadcast a message [35].…”

Section: Introductionmentioning

confidence: 99%

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Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations

Shah¹,

Martin²,

Coiera³

et al. 2019

J Med Internet Res

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Background Studies examining how sentiment on social media varies depending on timing and location appear to produce inconsistent results, making it hard to design systems that use sentiment to detect localized events for public health applications. Objective The aim of this study was to measure how common timing and location confounders explain variation in sentiment on Twitter. Methods Using a dataset of 16.54 million English-language tweets from 100 cities posted between July 13 and November 30, 2017, we estimated the positive and negative sentiment for each of the cities using a dictionary-based sentiment analysis and constructed models to explain the differences in sentiment using time of day, day of week, weather, city, and interaction type (conversations or broadcasting) as factors and found that all factors were independently associated with sentiment. Results In the full multivariable model of positive (Pearson r in test data 0.236; 95% CI 0.231-0.241) and negative (Pearson r in test data 0.306; 95% CI 0.301-0.310) sentiment, the city and time of day explained more of the variance than weather and day of week. Models that account for these confounders produce a different distribution and ranking of important events compared with models that do not account for these confounders. Conclusions In public health applications that aim to detect localized events by aggregating sentiment across populations of Twitter users, it is worthwhile accounting for baseline differences before looking for unexpected changes.

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Section: Comparisons With Past Literature and Implicationssupporting

confidence: 91%

Section: Weather Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations

Shah¹,

Martin²,

Coiera³

et al. 2019

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Vu et al (2014) learn emotion-specific event types by extracting emotion,event pairs on Twitter. Li et al (2014) uses Twitter to bootstrap 'major life events' and typical replies to those events. Ding and Riloff (2016) extract subj-verb-obj triples from blog posts.…”

Section: Related Workmentioning

confidence: 99%

“…One approach to this problem aims to directly learn units larger than a lexical item that reliably bear some marker of polarity or emotion (Vu et al, 2014;Li et al, 2014;Ding and Riloff, 2016;Goyal et al, 2010;Russo et al, 2015;Kiritchenko et al, 2014;Reckman et al, 2013). Another approach aims to model the speaker's affect to an event compositionally, e.g.…”

Section: Introductionmentioning

confidence: 99%

Learning Lexico-Functional Patterns for First-Person Affect

Reed

Oraby

et al. 2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 2: Short Papers)

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Informal first-person narratives are a unique resource for computational models of everyday events and people's affective reactions to them. People blogging about their day tend not to explicitly say I am happy. Instead they describe situations from which other humans can readily infer their affective reactions. However current sentiment dictionaries are missing much of the information needed to make similar inferences. We build on recent work that models affect in terms of lexical predicate functions and affect on the predicate's arguments. We present a method to learn proxies for these functions from firstperson narratives. We construct a novel fine-grained test set, and show that the patterns we learn improve our ability to predict first-person affective reactions to everyday events, from a Stanford sentiment baseline of .67F to .75F.

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Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Zhu

Zhang

et al. 2019

Geoscience Data Journal

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Crowdsourcing has significantly motivated the development of meteorological services. Starting from the beginning of 2010s and highly motivating after 2014, crowdsourcing-driven meteorological services have evolved from a single collection and observation of data to the systematic acquisition, analysis and application of these data. In this review, by focusing on papers and databases that have combined crowdsourcing methods to promote or implement meteorological knowledge services, we analysed the relevant literature in three dimensions: data collection, information analysis and meteorological knowledge applications. First, we selected the potential data sources for crowdsourcing and discussed the characteristics of the collected data in four dimensions: consciousness, objectiveness, mobility and multidisciplinary.Second, based on the purpose of these studies and the extent of utilizing data as well as knowledge, we categorize the crowdsourcing-based meteorological analysis into three levels: relationship discovery, knowledge generalization and systemized service. Third, according to the application scenario, we discussed the applications that have already been put into use, and we suggest current challenges and future research directions. These previous studies show that the use of crowdsourcing in social space can expand the coverage as well as enhance the performance of meteorological 62 | ZHU et al.

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What a Nasty Day

Cited by 30 publications

References 57 publications

Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations

Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations

Learning Lexico-Functional Patterns for First-Person Affect

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

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