Extracting ordinal temporal trail clusters in networks using symbolic time-series analysis

Gullapalli, Aparna R.; Carley, Kathleen M.

doi:10.1007/s13278-012-0091-7

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…We characterize profiles of user emotion transitions over time, observing patterns of "mood swings". Representing each user's sequence of emotion as a trail, we clustered all the trails together using a trail depth of 1 and a 60% similarity in the trail (Gullapalli and Carley, 2013), using the software ORA (Carley, 2014). A trail depth of 1 compares each emotion singularly and the similarity thresholds the length of trail overlap to be clustered together.…”

Section: Emotion Profilingmentioning

confidence: 99%

I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic

Ng¹,

Lee²,

Awal³

2020

Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

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With the world on a lockdown due to the COVID-19 pandemic, this paper studies emotions expressed on Twitter. Using a combined strategy of time series analysis of emotions augmented by tweet topics, this study provides an insight into emotion transitions during the pandemic. After tweets are annotated with dominant emotions and topics, a timeseries emotion analysis is used to identify disgust and anger as the most commonly identified emotions. Through longitudinal analysis of each user, we construct emotion transition graphs, observing key transitions between disgust and anger, and self-transitions within anger and disgust emotional states. Observing user patterns through clustering of user longitudinal analyses reveals emotional transitions fall into four main clusters: (1) erratic motion over short period of time, (2) disgust → anger, (3) optimism → joy. (4) erratic motion over a prolonged period. Finally, we propose a method for predicting users subsequent topic, and by consequence their emotions, through constructing an Emotion Topic Hidden Markov Model, augmenting emotion transition states with topic information. Results suggests that the predictions fare better than baselines, spurring directions of predicting emotional states based on Twitter posts.

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Section: Emotion Profilingmentioning

confidence: 99%

I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic

Ng¹,

Lee²,

Awal³

2020

Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

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“…Efficacy of the proposed algorithm is demonstrated by laboratory experimentation to model and then infer the hidden dynamics of different gaits for the T-hex walking robot. In [44], an algorithm to intuitively cluster groups of agent trails from networks based on STSA is proposed. The authors assert that temporal trails generated by agents traveling to various locations at different time epochs are becoming more prevalent in large social networks.…”

Section: Stsa In Applied Sciencesmentioning

confidence: 99%

Symbolic Time Series Analysis and Its Application in Social Sciences

Risso¹

2018

Time Series Analysis and Applications

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The present chapter intents to present the symbolic time series analysis (STSA) reviewing the recent developments in sciences. Even if there are very few works applied to social sciences, STSA has a potential to be developed. In particular, due to the limitations about historical data, fields such as Economics and Finance need to develop statistical tests to prove their hypotheses. An independence test and a causality test based on STSA are reviewed. They seem to be more powerful, detecting different kinds of nonlinearities compared with the classical tests, usually applied in social sciences. However, there is much work to do with STSA, and social sciences are a fertile field for the development of new powerful tools.

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