Listening between the Lines: Learning Personal Attributes from Conversations

Open-domain dialogue agents must be able to converse about many topics while incorporating knowledge about the user into the conversation. In this work we address the acquisition of such knowledge, for personalization in downstream Web applications, by extracting personal attributes from conversations. This problem is more challenging than the established task of information extraction from scientific publications or Wikipedia articles, because dialogues often give merely implicit cues about the speaker. We propose methods for inferring personal attributes, such as profession, age or family status, from conversations using deep learning. Specifically, we propose several Hidden Attribute Models, which are neural networks leveraging attention mechanisms and embeddings. Our methods are trained on a per-predicate basis to output rankings of object values for a given subject-predicate combination (e.g., ranking the doctor and nurse professions high when speakers talk about patients, emergency rooms, etc). Experiments with various conversational texts including Reddit discussions, movie scripts and a collection of crowdsourced personal dialogues demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines.Comment: published in WWW'1

A Survey on Prediction of Movie’s Box Office Collection Using Social Media

Predicting the box office profits of a movie prior to its world wide release are a significant but also an exigent problem that needs a advanced of Intelligence. Currently, social media has given away its diagnostic strength in a variety of fields, which encourages us to develop social media substance to predict box office profits. The collection of movies in provisions of profit relies on so many features for instance its making studio, type, screenplay superiority, pre release endorsement etc, each of which are usually utilized to approximation their probable achievement at the box office. Nevertheless, the “Wisdom of Crowd” and social media have been accredited as a powerful indication in appreciative customer activities to media. In this survey, we converse the influence of socially created Meta data derived from the social multimedia websites and review the effect of social media on box office collection and success of movies. This survey paper is written for (social networking) investigators who looking for to evaluate prediction of movies using social media. It gives a complete study of social media analytics for social networking, wikis, actually easy syndication feeds, blogs, newsgroups, chat and news feeds etc. Keywords: Social Networking, Social media. Movie’s Box Office, Prediction, Profitability, Sentiment analysi

Movie’s box office performance prediction: An approach based on movie’s script, text mining and deep learning

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceA capacidade de prever a bilheteria de filmes tem sido atividade de grande interesse para investigadores. Entretanto, parcela significativa destes estudos concentra-se no uso de variáveis disponíveis apenas nos estágios de produção e pós-produção de filmes. O objetivo deste trabalho é desenvolver um modelo preditivo de bilheteria baseando-se apenas em informações dos roteiros dos filmes, por meio do uso de técnicas de processamento de linguagem natural (PLN), mineração de texto e de redes neuronais profundas. Essa abordagem visa otimizar a tomada de decisão de investidores em uma fase ainda inicial dos projetos, com foco específico na melhoria dos processos seletivos da Agência Nacional do Cinema do Brasil.The ability to predict movies box-office has been a field of interest for many researchers. However, most of these studies are concentrated on variables that are available only in later stages as in production and pos-production phase of films. The objective of this work is to develop a predictive model to forecast movie box-office performance based only on information in the movie script, using natural language processing techniques, text mining and deep learning neural networks. This approach aims to optimize the investor’s decision-making process at earlier steps of the project, with special focus on the selection process of the Brazilian Film Agency (ANCINE – Agência Nacional do cinema)

Cinematographic Shot Classification with Deep Ensemble Learning

Cinematographic shot classification assigns a category to each shot either on the basis of the field size or on the movement performed by the camera. In this work, we focus on the camera field of view, which is determined by the portion of the subject and of the environment shown in the field of view of the camera. The automation of this task can help freelancers and studios belonging to the visual creative field in their daily activities. In our study, we took into account eight classes of film shots: long shot, medium shot, full figure, american shot, half figure, half torso, close up and extreme close up. The cinematographic shot classification is a complex task, so we combined state-of-the-art techniques to deal with it. Specifically, we finetuned three separated VGG-16 models and combined their predictions in order to obtain better performances by exploiting the stacking learning technique. Experimental results demonstrate the effectiveness of the proposed approach in performing the classification task with good accuracy. Our method was able to achieve 77% accuracy without relying on data augmentation techniques. We also evaluated our approach in terms of f1 score, precision, and recall and we showed confusion matrices to show that most of our misclassified samples belonged to a neighboring class