Dynamic evolution of sentiments in Never Let Me Go: Insights from quantitative analysis and implications The moods, feelings and attitudes represented in a novel will resonate in the reader by activating similar sentiments. It is generally accepted that sentiment analysis can capture aspects of such moods, feelings and attitudes and can be used to summarize a novelu0027s plot in a story arc. With the availability of a number of algorithms to automatically extract sentimentbased arcs, new approaches for their utilization becomes pertinent. We propose to use nonlinear adaptive filtering and fractal analysis in order to analyze the narrative coherence and dynamic evolution of a novel. We propose to use Never Let Me Go by Kazuo Ishiguro, the winner of the 2017 Nobel Prize for Literature as an example, we show that: 1) nonlinear adaptive filtering extracts a story arc that reflects the tragic trend of the novel; 2) The story arc displays persistent dynamics as measured by the Hurst exponent at short and medium time scales; 3) the plotsu0027 dynamic evolution is reflected in the timevarying Hurst exponent. We argue that these findings are indicative of the potential multifractal theory has for computational narratology and largescale literary analysis. Specifically, that the global Hurst exponent of a story arc is an index of narrative coherence that can identify bland, incoherent and coherent narratives on a continuous scale. And, further, that the local timevarying Hurst exponent captures variation of a novelu0027s plot such that the extreme have specific narratological interpretations.

Emotions Extracted from Text vs. True Emotionsxe2x80x93An Empirical Evaluation in SE Context Emotion awareness research in SE context has been growing in recent years. Currently, researchers often rely on textual communication records to extract emotion states using natural language processing techniques. However, how well these extracted emotion states reflect peopleu0027s real emotions has not been thoroughly investigated. In this paper, we report a multilevel, longitudinal empirical study with 82 individual members in 27 project teams. We collected their selfreported retrospective emotion states on a weekly basis during their yearlong projects and also extracted corresponding emotions from the textual communication records. We then model and compare the dynamics of these two types of emotions using multiple statistical and time series analysis methods. Our analyses yield a rich set of findings. The most important one is that the dynamics of emotions extracted using textbased algorithms often do not well reflect the dynamics of selfreported retrospective emotions. Besides, the extracted emotions match selfreported retrospective emotions better at the teamlevel. Our results also suggest that individual personalities and the teamu0027s emotion display norms significantly impact the matchmismatch. Our results should warn the research community about the limitations and challenges of applying textbased emotion recognition tools in SE research.