1,327 research outputs found
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
Text segmentation on multilabel documents: A distant-supervised approach
Segmenting text into semantically coherent segments is an important task with
applications in information retrieval and text summarization. Developing
accurate topical segmentation requires the availability of training data with
ground truth information at the segment level. However, generating such labeled
datasets, especially for applications in which the meaning of the labels is
user-defined, is expensive and time-consuming. In this paper, we develop an
approach that instead of using segment-level ground truth information, it
instead uses the set of labels that are associated with a document and are
easier to obtain as the training data essentially corresponds to a multilabel
dataset. Our method, which can be thought of as an instance of distant
supervision, improves upon the previous approaches by exploiting the fact that
consecutive sentences in a document tend to talk about the same topic, and
hence, probably belong to the same class. Experiments on the text segmentation
task on a variety of datasets show that the segmentation produced by our method
beats the competing approaches on four out of five datasets and performs at par
on the fifth dataset. On the multilabel text classification task, our method
performs at par with the competing approaches, while requiring significantly
less time to estimate than the competing approaches.Comment: Accepted in 2018 IEEE International Conference on Data Mining (ICDM
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions
Networks are powerful data structures, but are challenging to work with for
conventional machine learning methods. Network Embedding (NE) methods attempt
to resolve this by learning vector representations for the nodes, for
subsequent use in downstream machine learning tasks.
Link Prediction (LP) is one such downstream machine learning task that is an
important use case and popular benchmark for NE methods. Unfortunately, while
NE methods perform exceedingly well at this task, they are lacking in
transparency as compared to simpler LP approaches.
We introduce ExplaiNE, an approach to offer counterfactual explanations for
NE-based LP methods, by identifying existing links in the network that explain
the predicted links. ExplaiNE is applicable to a broad class of NE algorithms.
An extensive empirical evaluation for the NE method `Conditional Network
Embedding' in particular demonstrates its accuracy and scalability
A neural network approach to audio-assisted movie dialogue detection
A novel framework for audio-assisted dialogue detection based on indicator functions and neural networks is investigated. An indicator function defines that an actor is present at a particular time instant. The cross-correlation function of a pair of indicator functions and the magnitude of the corresponding cross-power spectral density are fed as input to neural networks for dialogue detection. Several types of artificial neural networks, including multilayer perceptrons, voted perceptrons, radial basis function networks, support vector machines, and particle swarm optimization-based multilayer perceptrons are tested. Experiments are carried out to validate the feasibility of the aforementioned approach by using ground-truth indicator functions determined by human observers on 6 different movies. A total of 41 dialogue instances and another 20 non-dialogue instances is employed. The average detection accuracy achieved is high, ranging between 84.78%±5.499% and 91.43%±4.239%
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