Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena

By Johan Bollen, Huina Mao, Alberto Pepe. International Conference on Web and Social Media, 2009

1,236 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/6298da6bfa0528bba0ffaf70e0dbe9ed223ce08f

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Citations in the corpus

Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior (2013, background): “Besides being increasingly used for political deliberation, twitter is said to be capable of reflecting collective emotive trends and thus might have the predictive power with regard to political events [10].”
Interpretation and trust: designing model-driven visualizations for text analysis (2012, background): “Other analyses infer latent topics [22, 23, 24, 25, 34, 46, 53], sentiment [5, 36, 51], or word relationships (e.”
Measuring Emotional Contagion in Social Media (2015, neutral): “[37, 6, 5, 31].”
CDAS: A Crowdsourcing Data Analytics System (2012, methodology): “TSA job is typically processed using machine learning and information retrieval techniques [2][22].”
On Using Twitter to Monitor Political Sentiment and Predict Election Results (2011, background): “Bollen et al. have focused on modeling public mood on a variety of axes to correlate with socio-economic factors (Bollen et al., 2009) and to predict the Dow Jones Industrial Average (Bollen et al., 2010).”
Comparing and combining sentiment analysis methods (2013, methodology): “ to compare eight of the most widely used methods. As a natural extension of this work, we would like to continue to add more existing methods for comparison, such as the Prole of Mood States (POMS) [8] and OpinionFinder [33]. Furthermore, we would like to expand the way we compare these methods by ”
How the Scientific Community Reacts to Newly Submitted Preprints: Article Downloads, Twitter Mentions, and Citations (2012, background): “The calculated correlation coefficients for various values of N in the interval [3, 100] are listed in Table 3.”
Emotional States vs. Emotional Words in Social Media (2015, background): “Others have created their own approaches based on word frequency to measure “mood” on Twitter [3, 2] and to create an index of collective “happiness” [9].”
Current State of Text Sentiment Analysis from Opinion to Emotion Mining (2017, background): “Bollen et al. [2011] analyze emotions of all tweets in a speciﬁc time frame.”
Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network (2013, methodology): “Bollen et al. (2011) introduced a six point sentiment scale.”
A survey of sentiment analysis in social media (2018, methodology): “The contents from Twitter have been used by researchers [16, 17, 81, 82].”
Detecting offensive tweets via topical feature discovery over a large scale twitter corpus (2012, background): “Twitter has drawn significant attention in recent years, and much work related to sentiment analysis has been published, such as [5, 4, 9].”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, methodology): “Bollen, Pepe, and Mao (2009) analyzed 9,664,952 tweets posted in the second half of 2008 using Profile of Mood States (POMS) (McNair, Lorr, & Droppleman, 1989).”