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

Role of sentiment classification in sentiment analysis: a survey

Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results

A survey on perceived speaker traits: personality, likability, pathology, and the first challenge

The INTERSPEECH 2012 Speaker Trait Challenge aimed at a unified test-bed for perceived speaker traits – the first challenge of this kind: personality in the five OCEAN personality dimensions, likability of speakers, and intelligibility of pathologic speakers. In the present article, we give a brief overview of the state-of-the-art in these three fields of research and describe the three sub-challenges in terms of the challenge conditions, the baseline results provided by the organisers, and a new openSMILE feature set, which has been used for computing the baselines and which has been provided to the participants. Furthermore, we summarise the approaches and the results presented by the participants to show the various techniques that are currently applied to solve these classification tasks

Face Emotion Recognition Based on Machine Learning: A Review

Computers can now detect, understand, and evaluate emotions thanks to recent developments in machine learning and information fusion. Researchers across various sectors are increasingly intrigued by emotion identification, utilizing facial expressions, words, body language, and posture as means of discerning an individual's emotions. Nevertheless, the effectiveness of the first three methods may be limited, as individuals can consciously or unconsciously suppress their true feelings. This article explores various feature extraction techniques, encompassing the development of machine learning classifiers like k-nearest neighbour, naive Bayesian, support vector machine, and random forest, in accordance with the established standard for emotion recognition. The paper has three primary objectives: firstly, to offer a comprehensive overview of effective computing by outlining essential theoretical concepts; secondly, to describe in detail the state-of-the-art in emotion recognition at the moment; and thirdly, to highlight important findings and conclusions from the literature, with an emphasis on important obstacles and possible future paths, especially in the creation of state-of-the-art machine learning algorithms for the identification of emotions

Big Data analytics to assess personality based on voice analysis

Trabajo Fin de Grado en Ingeniería de Tecnologías y Servicios de TelecomunicaciónWhen humans speak, the produced series of acoustic signs do not encode only the linguistic message they wish to communicate, but also several other types of information about themselves and their states that show glimpses of their personalities and can be apprehended by judgers. As there is nowadays a trend to film job candidate’s interviews, the aim of this Thesis is to explore possible correlations between speech features extracted from interviews and personality characteristics established by experts, and to try to predict in a candidate the Big Five personality traits: Conscientiousness, Agreeableness, Neuroticism, Openness to Experience and Extraversion. The features were extracted from a genuine database of 44 women video recordings acquired in 2020, and 78 in 2019 and before from a previous study. Even though many significant correlations were found for each years’ dataset, lots of them were proven to be inconsistent through both studies. Only extraversion, and openness in a more limited way, showed a good number of clear correlations. Essentially, extraversion has been found to be related to the variation in the slope of the pitch (usually at the end of sentences), which indicates that a more "singing" voice could be associated with a higher score. In addition, spectral entropy and roll-off measurements have also been found to indicate that larger changes in the spectrum (which may also be related to more "singing" voices) could be associated with greater extraversion too. Regarding predictive modelling algorithms, aimed to estimate personality traits from the speech features obtained for the study, results were observed to be very limited in terms of accuracy and RMSE, and also through scatter plots for regression models and confusion matrixes for classification evaluation. Nevertheless, various results encourage to believe that there are some predicting capabilities, and extraversion and openness also ended up being the most predictable personality traits. Better outcomes were achieved when predictions were performed based on one specific feature instead of all of them or a reduced group, as it was the case for openness when estimated through linear and logistic regression based on time over 90% of the variation range of the deltas from the entropy of the spectrum module. Extraversion too, as it correlates well with features relating variation in F0 decreasing slope and variations in the spectrum. For the predictions, several machine learning algorithms have been used, such as linear regression, logistic regression and random forests