Audio-Visual Sentiment Analysis for Learning Emotional Arcs in Movies

Stories can have tremendous power -- not only useful for entertainment, they can activate our interests and mobilize our actions. The degree to which a story resonates with its audience may be in part reflected in the emotional journey it takes the audience upon. In this paper, we use machine learning methods to construct emotional arcs in movies, calculate families of arcs, and demonstrate the ability for certain arcs to predict audience engagement. The system is applied to Hollywood films and high quality shorts found on the web. We begin by using deep convolutional neural networks for audio and visual sentiment analysis. These models are trained on both new and existing large-scale datasets, after which they can be used to compute separate audio and visual emotional arcs. We then crowdsource annotations for 30-second video clips extracted from highs and lows in the arcs in order to assess the micro-level precision of the system, with precision measured in terms of agreement in polarity between the system's predictions and annotators' ratings. These annotations are also used to combine the audio and visual predictions. Next, we look at macro-level characterizations of movies by investigating whether there exist `universal shapes' of emotional arcs. In particular, we develop a clustering approach to discover distinct classes of emotional arcs. Finally, we show on a sample corpus of short web videos that certain emotional arcs are statistically significant predictors of the number of comments a video receives. These results suggest that the emotional arcs learned by our approach successfully represent macroscopic aspects of a video story that drive audience engagement. Such machine understanding could be used to predict audience reactions to video stories, ultimately improving our ability as storytellers to communicate with each other.Comment: Data Mining (ICDM), 2017 IEEE 17th International Conference o

Bunker Cave stalagmites: an archive for central European Holocene climate variability

Holocene climate was characterised by variability on multi-centennial to multi-decadal time scales. In central Europe, these fluctuations were most pronounced during winter. Here we present a record of past winter climate variability for the last 10.8 ka based on four speleothems from Bunker Cave, western Germany. Due to its central European location, the cave site is particularly well suited to record changes in precipitation and temperature in response to changes in the North Atlantic realm. We present high-resolution records of δ18O, δ13C values and Mg/Ca ratios. Changes in the Mg/Ca ratio are attributed to past meteoric precipitation variability. The stable C isotope composition of the speleothems most likely reflects changes in vegetation and precipitation, and variations in the δ18O signal are interpreted as variations in meteoric precipitation and temperature. We found cold and dry periods between 8 and 7 ka, 6.5 and 5.5 ka, 4 and 3 ka as well as between 0.7 and 0.2 ka. The proxy signals in the Bunker Cave stalagmites compare well with other isotope records and, thus, seem representative for central European Holocene climate variability. The prominent 8.2 ka event and the Little Ice Age cold events are both recorded in the Bunker Cave record. However, these events show a contrasting relationship between climate and δ18O, which is explained by different causes underlying the two climate anomalies. Whereas the Little Ice Age is attributed to a pronounced negative phase of the North Atlantic Oscillation, the 8.2 ka event was triggered by cooler conditions in the North Atlantic due to a slowdown of the thermohaline circulation