Emotional content is a crucial ingredient in user-generated videos. However, the sparsely expressed emotions in the user-generated video cause difficulties to emotions analysis in videos. In this paper, we propose a new neural approach---Bi-stream Emotion Attribution-Classification Network (BEAC-Net) to solve three related emotion analysis tasks: emotion recognition, emotion attribution and emotion-oriented summarization, in an integrated framework. BEAC-Net has two major constituents, an attribution network and a classification network. The attribution network extracts the main emotional segment that classification should focus on in order to mitigate the sparsity problem. The classification network utilizes both the extracted segment and the original video in a bi-stream architecture. We contribute a new dataset for the emotion attribution task with human-annotated ground-truth labels for emotion segments. Experiments on two video datasets demonstrate superior performance of the proposed framework and the complementary nature of the dual classification streams.
By analyzing linguistic markers in social media posts, it's possible to create a deep learning model that can give an individual insight into his or her mental health far earlier than traditional approaches. More than 300 million people suffer from depression and only a fraction of them receive adequate treatment. Depression is the leading cause of disability worldwide and nearly 800,000 people every year die due to suicide. Suicide is the second leading cause of death in 15–29-year-olds. Diagnoses (and subsequent treatment) for depression are often delayed, imprecise, and/or missed entirely.
One of the best way to get better at machine learning and deep learning is to watch a lecture from an expert and work your way along with it. If you do so, you get the best of both the worlds – you learn from the experts across the globe and also get hands on knowledge. In this article, I have provided a list of YouTube videos, which you can use to improve your knowledge in these areas. You've got to follow a ritual (Just Kidding!). For your ease, I have created a'to be followed' sequence / order of these videos.
Automatic summarization of natural language is a current topic in computer science research and industry, studied for decades because of its usefulness across multiple domains. For example, summarization is necessary to create reviews such as this one. Research and applications have achieved some success in extractive summarization (where key sentences are curated), however, abstractive summarization (synthesis and re-stating) is a hard problem and generally unsolved in computer science. This literature review contrasts historical progress up through current state of the art, comparing dimensions such as: extractive vs. abstractive, supervised vs. unsupervised, NLP (Natural Language Processing) vs Knowledge-based, deep learning vs algorithms, structured vs. unstructured sources, and measurement metrics such as Rouge and BLEU. Multiple dimensions are contrasted since current research uses combinations of approaches as seen in the review matrix. Throughout this summary, synthesis and critique is provided. This review concludes with insights for improved abstractive summarization measurement, with surprising implications for detecting understanding and comprehension in general.
Deep learning also known as deep structured learning or hierarchical learning is part of machine learning based on artificial neural networks. This learning methodology can be supervised, semi-supervised or unsupervised. Deep learning architectures such as neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation where they have produced results comparable to and in some cases superior to human experts. They used a combination of algorithms and mathematics they called "threshold logic" to mimic the thought process. Since that time, Deep Learning has evolved steadily, with only two significant breaks in its development.