Multimodal Content Analysis for Effective Advertisements on YouTube

The rapid advances in e-commerce and Web 2.0 technologies have greatly increased the impact of commercial advertisements on the general public. As a key enabling technology, a multitude of recommender systems exists which analyzes user features and browsing patterns to recommend appealing advertisements to users. In this work, we seek to study the characteristics or attributes that characterize an effective advertisement and recommend a useful set of features to aid the designing and production processes of commercial advertisements. We analyze the temporal patterns from multimedia content of advertisement videos including auditory, visual and textual components, and study their individual roles and synergies in the success of an advertisement. The objective of this work is then to measure the effectiveness of an advertisement, and to recommend a useful set of features to advertisement designers to make it more successful and approachable to users. Our proposed framework employs the signal processing technique of cross modality feature learning where data streams from different components are employed to train separate neural network models and are then fused together to learn a shared representation. Subsequently, a neural network model trained on this joint feature embedding representation is utilized as a classifier to predict advertisement effectiveness. We validate our approach using subjective ratings from a dedicated user study, the sentiment strength of online viewer comments, and a viewer opinion metric of the ratio of the Likes and Views received by each advertisement from an online platform.Comment: 11 pages, 5 figures, ICDM 201

Affect Recognition in Ads with Application to Computational Advertising

Advertisements (ads) often include strongly emotional content to leave a lasting impression on the viewer. This work (i) compiles an affective ad dataset capable of evoking coherent emotions across users, as determined from the affective opinions of five experts and 14 annotators; (ii) explores the efficacy of convolutional neural network (CNN) features for encoding emotions, and observes that CNN features outperform low-level audio-visual emotion descriptors upon extensive experimentation; and (iii) demonstrates how enhanced affect prediction facilitates computational advertising, and leads to better viewing experience while watching an online video stream embedded with ads based on a study involving 17 users. We model ad emotions based on subjective human opinions as well as objective multimodal features, and show how effectively modeling ad emotions can positively impact a real-life application.Comment: Accepted at the ACM International Conference on Multimedia (ACM MM) 201

Text-based Sentiment Analysis and Music Emotion Recognition

Nowadays, with the expansion of social media, large amounts of user-generated texts like tweets, blog posts or product reviews are shared online. Sentiment polarity analysis of such texts has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. We also witness deep learning techniques becoming top performers on those types of tasks. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First of all, deep neural networks are data hungry. They need to be fed with datasets that are big in size, cleaned and preprocessed as well as properly labeled. Second, the modern natural language processing concept of word embeddings as a dense and distributed text feature representation solves sparsity and dimensionality problems of the traditional bag-of-words model. Still, there are various uncertainties regarding the use of word vectors: should they be generated from the same dataset that is used to train the model or it is better to source them from big and popular collections that work as generic text feature representations? Third, it is not easy for practitioners to find a simple and highly effective deep learning setup for various document lengths and types. Recurrent neural networks are weak with longer texts and optimal convolution-pooling combinations are not easily conceived. It is thus convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song datasets are created utilizing social tags. One of the research interests of Telecom Italia was the exploration of relations between music emotional stimulation and driving style. Consequently, a context-aware music recommender system that aims to enhance driving comfort and safety was also designed. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted. Word embeddings of different parameters were exercised and results revealed that their quality is influenced (mostly but not only) by the size of texts they were created from. When working with small text datasets, it is thus important to source word features from popular and generic word embedding collections. Regarding the third problem, a series of experiments involving convolutional and max-pooling neural layers were conducted. Various patterns relating text properties and network parameters with optimal classification accuracy were observed. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews. Given that labeled data are becoming the bottleneck of the current deep learning systems, a future research direction could be the exploration of various data programming possibilities for constructing even bigger labeled datasets. Investigation of feature-level or decision-level ensemble techniques in the context of deep neural networks could also be fruitful. Different feature types do usually represent complementary characteristics of data. Combining word embedding and traditional text features or utilizing recurrent networks on document splits and then aggregating the predictions could further increase prediction accuracy of such models

Investigating the Role of Emotion Perception in the Adaptive Functioning of Individuals on the Autism Spectrum

Cognitive functioning has historically been used to predict adaptive outcomes of individuals with autism spectrum disorders; however, research shows that it does not adequately predict these outcomes. Therefore, the current study explored the role of emotion perception in the adaptive functioning of individuals with ASDs. Emotion perception was assessed using the DANVA-2, which has audio and static face stimuli, and the DAVE, dynamic, audio-visual emotion movies. Adaptive functioning was assessed using the Vineland-II Socialization, Communication, and Daily Living domains. Results indicated that individuals with ASDs demonstrated significant impairments in both adaptive functioning and emotion perception compared to typical individuals. Findings did not demonstrate a relationship between emotion perception and adaptive functioning, controlling for IQ. Future research should broaden the approach when investigating possible mechanisms of change for adaptive outcomes to include exploration of social perception more broadly, of which emotion perception is one component, and its relationship with adaptive outcomes

Spontaneous blink rate as an index of attention and emotion during film clips viewing

Spontaneous blinking is a non-invasive indicator known to reflect dopaminergic influence over frontal cortex and attention allocation in perceptual tasks. 38 participants watched eighteen short film clips (2 min), designed to elicit specific affective states, and arranged in six different emotional categories, while their eye movements were recorded from the vertical electroculogram. The largest blink rate inhibition, reflecting greater attention allocation to the movie, was observed during the presentation of Erotic clips, excerpts on wilderness depicting beautiful landscapes (Scenery), as well as clips showing crying characters (Compassion). Instead, the minimum blink rate inhibition was found for Fear clips, which induced a defensive response with stimulus rejection. Blink rate across time evidenced how Compassion clips elicited early inhibition while Sadness clips induced a slower, later inhibition. Correlation analyses also revealed a negative correlation (r < -0.40) between total blink rate recorded during Erotic and Compassion clips and self-reported interest. Overall, the main variable explaining blink rate was emotional Valence. Results suggest that blink modulation is related with the motivational relevance and biological significance of the stimuli, tracking their differential recruitment of attentional resources. Furthermore, they provide a solid background for studying the emotion-attention patterns and their deficits also in clinical samples (e.g., neurological and psychiatric patients) using spontaneous blinking as a not-interfering psychophysiological measure