Understanding emotions is an important aspect of personal development and growth, and as such it is a key tile for the emulation of human intelligence. Besides being important for the advancement of AI, emotion processing is also important for the closely related task of polarity detection. The opportunity to automatically capture the general public's sentiments about social events, political movements, marketing campaigns, and product preferences has raised interest in both the scientific community, for the exciting open challenges, and the business world, for the remarkable fallouts in marketing and financial market prediction. This has led to the emerging fields of affective computing and sentiment analysis, which leverage human-computer interaction, information retrieval, and multimodal signal processing for distilling people's sentiments from the ever-growing amount of online social data.

Affective Computing and Sentiment Analysis

Happiness and other emotions spread between people in direct contact, but it is unclear whether massive online social networks also contribute to this spread. Here, we elaborate a novel method for measuring the contagion of emotional expression. With data from millions of Facebook users, we show that rainfall directly influences the emotional content of their status messages, and it also affects the status messages of friends in other cities who are not experiencing rainfall. For every one person affected directly, rainfall alters the emotional expression of about one to two other people, suggesting that online social networks may magnify the intensity of global emotional synchrony.

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Detecting Emotional Contagion in Massive Social Networks

Social media have received more attention nowadays. Public and private opinion about a wide variety of subjects are expressed and spread continually via numerous social media. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyze customers' perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers' perceptions. This paper reports on the design of a sentiment analysis, extracting a vast amount of tweets. Prototyping is used in this development. Results classify customers' perspective via tweets into positive and negative, which is represented in a pie chart and html page. However, the program has planned to develop on a web application system, but due to limitation of Django which can be worked on a Linux server or LAMP, for further this approach need to be done.

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Twitter sentiment analysis

A survey of multimodal sentiment analysis

Big data: Dimensions, evolution, impacts, and challenges

/pdf/estudo-economico-do-cultivo-de-soja-com-safrinha-de-milho-e-4y25imzs0p.pdf

Estudo econômico do cultivo de soja com safrinha de milho e girassol

1078 Machine Learning to Predict PAP Adherence and Compliance in Tele-Health Management

The problem of consistent therapy adherence is a current challenge for health informatics, and its solution can increase the success rate of treatments. Here we show a methodology to predict, at individual-level, future therapy adherence for patients receiving daily injections of growth hormone (GH) therapy for GH deficiency. Our proposed model is able to generate predictions of future adherence using a recurrent neural network with adherence data recorded by easypodTM, a connected autoinjection device. The model was trained with a multi-year long dataset with 2500 patients, from January 2007 to June 2019. When testing, the model reached an average sensitivity of 0.70 and a specificity of 0.88 per patient when predicting non-adherence (<85%) periods. When evaluated with thousands of therapy segments extracted from a test set, our model reached an AUC-PR score of 0.79 and AUC-ROC of 0.90; both metrics were consistently better than traditional approaches, such as simple average model. Using this model, we can perform precise early identification of patients who are likely to become non-adherent patients. This opens a path for healthcare practitioners to personalize GH therapy at any stage of the patients' journey and improve shared decision making with patients and caregivers to achieve optimal outcomes.

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Using Deep Learning for Individual-Level Predictions of Adherence with Growth Hormone Therapy.

The abandonment rate of patients who use CPAP devices for obstructive sleep apnea (OSA) therapy is as high as 60%. However, there is growing evidence that timely and appropriate intervention can improve long-term adherence to therapy. Current practice in sleep clinics of identifying potential patients who will abandon the treatment is not sufficiently effective in terms of accuracy and timeliness. Recent proposals in the literature have tried to identify non-adherent patients in a specific period of their therapy; however, there is no generalized approach by which clinical providers can monitor their patients continually with the goal of maximizing adherence. Towards this more generic goal, we propose CTAP-CPAP, a Continuous Treatment Adherence Prediction framework. With CTAP-CPAP, we address the problem of generalizing the prediction for any day in the treatment, where a robust framework with multiple machine learning models is implemented to assist medical practitioners keep track of the patient risk of non-adherence. Aiming the parallel progress of both machine learning and health informatics fields, we complement the study with a transparent discussion on the machine learning techniques used to build CTAP-CPAP and our view of its operationalization in a sleep clinic.

A Data-Driven Approach for Continuous Adherence Predictions in Sleep Apnea Therapy Management

The Internet deeply changed the way people share their knowledge. Almost all content that people produces is now available in digital formats, like e-books, apps, newspapers, and magazines. That content has commonly some metadata available that can be used to generate complex recommendation systems that track content similarity. Since there is some effort in the literature to explore this direction, almost all use classical recommendation approaches, like collaborative filter data and information present on websites that sells books. While most efforts in the literature use features derived from the text syntax to create a recommendation model, our approach aims to trace an emotional fingerprint of authors extracted from their texts. This approach, known as psychometry, consists of the study of behavioral characteristics like positivity, negativity, sadness, fear, religiosity, sexuality, which are able to disguise individuals. Using two sentiment analysis lexicons and a collection of 641 books from the English literature written by 56 authors, we show the effectiveness of these psychometric features in order to trace those authors emotional fingerprint.

Matheus Araújo

Papers

Estudo econômico do cultivo de soja com safrinha de milho e girassol

1078 Machine Learning to Predict PAP Adherence and Compliance in Tele-Health Management

Using Deep Learning for Individual-Level Predictions of Adherence with Growth Hormone Therapy.

A Data-Driven Approach for Continuous Adherence Predictions in Sleep Apnea Therapy Management

Emotional Fingerprint from Authors in Classical Literature