Tactics to determine the emotions of authors of texts such as Twitter messages often rely on multiple annotators who label relatively small data sets of text passages. An alternative method gathers large text databases that contain the authors’ self-reported emotions, to which artificial intelligence, machine learning, and natural language processing tools can be applied. Both approaches have strength and weaknesses. Emotions evaluated by a few human annotators are susceptible to idiosyncratic biases that reflect the characteristics of the annotators. But models based on large, self-reported emotion data sets may overlook subtle, social emotions that human annotators can recognize. In seeking to establish a means to train emotion detection models so that they can achieve good performance in different contexts, the current study proposes a novel transformer transfer learning approach that parallels human development stages: (1) detect emotions reported by the texts’ authors and (2) synchronize the model with social emotions identified in annotator-rated emotion data sets. The analysis, based on a large, novel, self-reported emotion data set (n = 3,654,544) and applied to 10 previously published data sets, shows that the transfer learning emotion model achieves relatively strong performance.
Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience, and computer science. This stems from its high degree of subjectivity and limited input sources that can effectively capture the actual sentiment. This can be even more challenging with only text-based input. Meanwhile, the rise of deep learning and an unprecedented large volume of data have paved the way for artificial intelligence to perform impressively accurate predictions or even human-level reasoning. Drawing inspiration from this, we propose a coverage-based sentiment and subsentence extraction system that estimates a span of input text and recursively feeds this information back to the networks. The predicted subsentence consists of auxiliary information expressing a sentiment. This is an important building block for enabling vivid and epic sentiment delivery (within the scope of this paper) and for other natural language processing tasks such as text summarisation and Q&A. Our approach outperforms the state-of-the-art approaches by a large margin in subsentence prediction (i.e., Average Jaccard scores from 0.72 to 0.89). For the evaluation, we designed rigorous experiments consisting of 24 ablation studies. Finally, our learned lessons are returned to the community by sharing software packages and a public dataset that can reproduce the results presented in this paper.
Mindfulness challenges allow consumers to track meditation frequency through posting social media updates documenting their regular meditations. However, little is known about the effects of mindfulness on consumers in these representative online settings. In one study (1a and 1b) the research utilises two types of data to explore how a contemplative practice such as mindfulness influences online behaviour. Specifically, consumers who have completed a 60-day online meditation challenge showed an increase (vs. decrease) in original tweets (vs. retweets) (study 1a), and further, consumers who completed the challenge (vs. did not complete) showed higher (vs. lower) positive sentiment of original tweets. Despite some research showing engagement in social media as maladaptive, we provide a positive and unexpected contribution to show that mindfulness has a positive effect on how consumers may engage with social media. Further, we contribute a novel research method based on Twitter that advances immediate and unique marketing methods. Finally, we expand the practical application of mindfulness by exploring how consumers are organically, and consequentially, practicing mindfulness in field settings.
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