Visual Attention and Emotion Analysis Based on Qualitative Assessment and Eyetracking Metrics-The Perception of a Video Game Trailer

Video game trailers are very useful tools for attracting potential players. This research focuses on analyzing the emotions that arise while viewing video game trailers and the link between these emotions and storytelling and visual attention. The methodology consisted of a three-step task test with potential users: the first step was to identify the perception of indie games; the second step was to use the eyetracking device (gaze plot, heat map, and fixation points) and link them to fixation points (attention), viewing patterns, and non-visible areas; the third step was to interview users to understand impressions and questionnaires of emotions related to the trailer's storytelling and expectations. The results show an effective assessment of visual attention together with visualization patterns, non-visible areas that may affect game expectations, fixation points linked to very specific emotions, and perceived narratives based on the gaze plot. The innovation in the mixed methodological approach has made it possible to obtain relevant data regarding the link between the emotions perceived by the user and the areas of attention collected with the device. The proposed methodology enables developers to understand the strengths and weaknesses of the information being conveyed so that they can tailor the trailer to the expectations of potential players.

Users opinion and emotion understanding in social media regarding COVID-19 vaccine

Online social platforms or social platforms such as Twitter, Facebook and Instagram have become popular platforms for a public discussion about social topics. Recent studies show that there is a growing tendency for people to talk about COVID-19 pandemic in these online channels. The rapid growth of the infected cases by COVID-19 pandemic makes a lots of anxiety and fear among people. With the recent released of Pfizer vaccine, people start posting a lot of rumors regarding the safety concerns of the vaccine, especially among the elderly people. The aim of this study is to bring out the fact that tweets containing all pertinent details about the COVID-19 vaccine and provides an analysis and understanding of users emotions regarding the recent release of COVID-19 vaccine. This study starts with the collection of tweets related to COVID-19 vaccine and then cleaning the dataset from redundant tweets. In this study, we use Twitter API and Web Scraping techniques to obtain a sample of 50,000 tweets talking about COVID-19 vaccine.Further, The analysis of users emotions is achieved by manually labeling and classifying the tweets to either positive or negative. Then, a deep learning based model is used to train the data and classify the people opinion about COVID-19 vaccine. The experimental results illustrate that high percentage of people have shown a positive attitude towards COVID1-19 vaccine. The proposed method is validated over Twitter datasets and the results also demonstrate that use of deep learning classifier can successfully improve the accuracy of people emotions analysis with an accuracy up to 98% for training set and the accuracy for testing set is 73%.

Determination of Patient Sentiment and Emotion in Ophthalmology: Infoveillance Tutorial on Web-Based Health Forum Discussions

Background

Clinical data in social media are an underused source of information with great potential to allow for a deeper understanding of patient values, attitudes, and preferences.

Objective

This tutorial aims to describe a novel, robust, and modular method for the sentiment analysis and emotion detection of free text from web-based forums and the factors to consider during its application.

Methods

We mined the discussion and user information of all posts containing search terms related to a medical subspecialty (oculoplastics) from MedHelp, the largest web-based platform for patient health forums. We used data cleaning and processing tools to define the relevant subset of results and prepare them for sentiment analysis. We executed sentiment and emotion analyses by using IBM Watson Natural Language Understanding to generate sentiment and emotion scores for the posts and their associated keywords. The keywords were aggregated using natural language processing tools.

Results

Overall, 39 oculoplastic-related search terms resulted in 46,381 eligible posts within 14,329 threads. Posts were written by 18,319 users (117 doctors; 18,202 patients) and included 201,611 associated keywords. Keywords that occurred ≥500 times in the corpus were used to identify the most prominent topics, including specific symptoms, medication, and complications. The sentiment and emotion scores of these keywords and eligible posts were analyzed to provide concrete examples of the potential of this methodology to allow for a better understanding of patients’ attitudes. The overall sentiment score reflects a positive, neutral, or negative sentiment, whereas the emotion scores (anger, disgust, fear, joy, and sadness) represent the likelihood of the presence of the emotion. In keyword grouping analyses, medical signs, symptoms, and diseases had the lowest overall sentiment scores (−0.598). Complications were highly associated with sadness (0.485). Forum posts mentioning body parts were related to sadness (0.416) and fear (0.321). Administration was the category with the highest anger score (0.146). The top 6 forum subgroups had an overall negative sentiment score; the most negative one was the Neurology forum, with a score of −0.438. The Undiagnosed Symptoms forum had the highest sadness score (0.448). The least likely fearful posts were those from the Eye Care forum, with a score of 0.260. The overall sentiment score was much more negative before the doctor replied. The anger, disgust, fear, and sadness emotion scores decreased in likelihood, whereas joy was slightly more likely to be expressed after doctors replied.

Conclusions

This report allows physicians and researchers to efficiently mine and perform sentiment analysis on social media to better understand patients’ perspectives and promote patient-centric care. Important factors to be considered during its application include evaluating the scope of the search; selecting search terms and understanding their linguistic usages; and establishing selection, filtering, and processing criteria for posts and keywords tailored to the desired results.

Hybrid System of Emotion Evaluation in Physiotherapeutic Procedures

Nowadays, the dynamic development of technology allows for the design of systems based on various information sources and their integration into hybrid expert systems. One of the areas of research where such systems are especially helpful is emotion analysis. The sympathetic nervous system controls emotions, while its function is directly reflected by the electrodermal activity (EDA) signal. The presented study aimed to develop a tool and propose a physiological data set to complement the psychological data. The study group consisted of 41 students aged from 19 to 26 years. The presented research protocol was based on the acquisition of the electrodermal activity signal using the Empatica E4 device during three exercises performed in a prototype Disc4Spine system and using the psychological research methods. Different methods (hierarchical and non-hierarchical) of subsequent data clustering and optimisation in the context of emotions experienced were analysed. The best results were obtained for the k-means classifier during Exercise 3 (80.49%) and for the combination of the EDA signal with negative emotions (80.48%). A comparison of accuracy of the k-means classification with the independent division made by a psychologist revealed again the best results for negative emotions (78.05%).