SSentiaA: A Self-Supervised Sentiment Analyzer for Classification From Unlabeled Data

In recent years, supervised machine learning (ML) methods have realized remarkable performance gains for sentiment classification utilizing labeled data. However, labeled data are usually expensive to obtain, thus, not always achievable. When annotated data are unavailable, the unsupervised tools are exercised, which still lag behind the performance of supervised ML methods by a large margin. Therefore, in this work, we focus on improving the performance of sentiment classification from unlabeled data. We present a self-supervised hybrid methodology SSentiA (Self-supervised Sentiment Analyzer) that couples an ML classifier with a lexicon-based method for sentiment classification from unlabeled data. We first introduce LRSentiA (Lexical Rule-based Sentiment Analyzer), a lexicon-based method to predict the semantic orientation of a review along with the confidence score of prediction. Utilizing the confidence scores of LRSentiA, we generate highly accurate pseudo-labels for SSentiA that incorporates a supervised ML algorithm to improve the performance of sentiment classification for less polarized and complex reviews. We compare the performances of LRSentiA and SSSentA with the existing unsupervised, lexicon-based and self-supervised methods in multiple datasets. The LRSentiA performs similarly to the existing lexicon-based methods in both binary and 3-class sentiment analysis. By combining LRSentiA with an ML classifier, the hybrid approach SSentiA attains 10%–30% improvements in macro F1 score for both binary and 3-class sentiment analysis. The results suggest that in domains where annotated data are unavailable, SSentiA can significantly improve the performance of sentiment classification. Moreover, we demonstrate that using 30%–60% annotated training data, SSentiA delivers similar performances of the fully labeled training dataset

Automated Filtering of Eye Movements Using Dynamic AOI in Multiple Granularity Levels

Eye-tracking experiments involve areas of interest (AOIs) for the analysis of eye gaze data. While there are tools to delineate AOIs to extract eye movement data, they may require users to manually draw boundaries of AOIs on eye tracking stimuli or use markers to define AOIs. This paper introduces two novel techniques to dynamically filter eye movement data from AOIs for the analysis of eye metrics from multiple levels of granularity. The authors incorporate pre-trained object detectors and object instance segmentation models for offline detection of dynamic AOIs in video streams. This research presents the implementation and evaluation of object detectors and object instance segmentation models to find the best model to be integrated in a real-time eye movement analysis pipeline. The authors filter gaze data that falls within the polygonal boundaries of detected dynamic AOIs and apply object detector to find bounding-boxes in a public dataset. The results indicate that the dynamic AOIs generated by object detectors capture 60% of eye movements & object instance segmentation models capture 30% of eye movements

DFS: A Dataset File System for Data Discovering Users

Many research questions can be answered quickly and efficiently using data already collected for previous research. This practice is called secondary data analysis (SDA), and has gained popularity due to lower costs and improved research efficiency. In this paper we propose DFS, a file system to standardize the metadata representation of datasets, and DDU, a scalable architecture based on DFS for semi-automated metadata generation and data recommendation on the cloud. We discuss how DFS and DDU lays groundwork for automatic dataset aggregation, how it integrates with existing data wrangling and machine learning tools, and explores their implications on datasets stored in digital libraries

X-DisETrac: Distributed Eye-Tracking with Extended Realities

Humans use heterogeneous collaboration mediums such as in-person, online, and extended realities for day-to-day activities. Identifying patterns in viewpoints and pupillary responses (a.k.a eye-tracking data) provide informative cues on individual and collective behavior during collaborative tasks. Despite the increasing ubiquity of these different mediums, the aggregation and analysis of eye-tracking data in heterogeneous collaborative environments remain unexplored. Our study proposes X-DisETrac: Extended Distributed Eye Tracking, a versatile framework for eye tracking in heterogeneous environments. Our approach tackles the complexity by establishing a platform-agnostic communication protocol encompassing three data streams to simplify data aggregation and analytics. Our study establishes seminal work in multi-user eye-tracking in heterogeneous environments.https://digitalcommons.odu.edu/gradposters2023_sciences/1010/thumbnail.jp

Human Interaction With Fake News

https://digitalcommons.odu.edu/reu2022_computerscience/1007/thumbnail.jp

ADHD Prediction Through Analysis of Eye Movements With Graph Convolution Network

Processing speech with background noise requires appropriate parsing of the distorted auditory signal, fundamental language abilities as well as higher signal-to-noise ratio. Adolescents with ADHD have difficulty processing speech with background noise due to reduced inhibitory control and working memory capacity. In this study we utilize Audiovisual Speech-In-Noise performance and eye-tracking measures of young adults with ADHD compared to age-matched controls, and generate graphs for ADHD evaluation using the eye-tracking data. We form graphs utilizing the eight eye-tracking features (fixation count, average, total, and standard deviation of fixation duration, max and min saccade peak velocity, min, average, and standard deviation of saccade amplitude), and connection among trials in terms of subject, background noise, and sentence. We created multiple un-directed multi- graphs, each with 830 nodes which corresponds to a trial. Each trial is defined by a participant, background noise-level, and the sentence the participant was presented. For instance, k th node has information of {‘background noise level’: x, ‘sentence’: i, ‘subject’: j }. For each node, we create a feature matrix utilizing aforementioned eight eye gaze metrics. Links between pair of nodes mean that they belong to the same edge category. We introduced different types of edge categories: Same Background Noise Level, Same Subject, Same Sentence, Same Background Noise Level and Same Subject, Same Subject and Same Sentence, and Same Background Noise Level and Same Sentence. In our Graph Convolutional Network (GCN) model, we use node embedding and adjacency matrix representation as the input. The GCN layer is a multiplication of inputs, weights, and the normalized adjacency matrix. From the results we observed that only “Same Background Noise Level and Same Subject” edge category was able to give slightly better results in terms of AUC ROC and Precision. Additionally, we visualized what the model has learned by accessing the embeddings before the classification layer.https://digitalcommons.odu.edu/gradposters2023_sciences/1023/thumbnail.jp

Analysis of Reading Patterns of Scientific Literature Using Eye-Tracking Measures

Scientific literature is crucial for researchers to inspire novel research ideas and find solutions to various problems. This study presents a reading task for novice researchers using eye-tracking measures. The study focused on the scan paths, fixation, and pupil dilation frequency of the participants. In this study, 3 participants were asked to read a pre-selected research paper while wearing an eye-tracking device (PupilLabs Core 200Hz). We specified sections of the research paper as areas of interest (title, abstract, motivation, methodology, conclusion)to analyze the eye-movements. Then we extracted eye-movements data from the recordings and processed them using an eye-movement processing pipeline. To analyze how the eye-movements change throughout the reading task, we calculated fixation counts, fixation duration, and index of pupillary activity (IPA) for each participant. IPA is calculated using the pupil diameter and low IPA reflects low cognitive load, whereas high IPA reflects strong cognitive load. When analyzing scan paths, we observed that all participants started reading from the title section of the paper. Following this, no two participants followed the same scan path when reading the paper. Also, the average fixation counts and duration suggested that participants preferred to fixate more on the methodology section and spent more time reading it compared to the other sections. Moreover, the IPA of participants was higher when reading the title section, indicating higher cognitive demand prior to exploring the research idea presented in the paper. The least IPA was observed in the methodology section, indicating a lower cognitive load. The purpose of this study was to analyze the scan paths of novice researchers while reading a research paper. We observed different scan paths among participants, and a higher fixation count and duration when reading the methodology section, with a comparatively low cognitive load.https://digitalcommons.odu.edu/gradposters2021_sciences/1001/thumbnail.jp

Predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity

ADHD is being recognized as a diagnosis that persists into adulthood impacting educational and economic outcomes. There is an increased need to accurately diagnose this population through the development of reliable and valid outcome measures reflecting core diagnostic criteria. For example, adults with ADHD have reduced working memory capacity (WMC) when compared to their peers. A reduction in WMC indicates attention control deficits which align with many symptoms outlined on behavioral checklists used to diagnose ADHD. Using computational methods, such as machine learning, to generate a relationship between ADHD and measures of WMC would be useful to advancing our understanding and treatment of ADHD in adults. This chapter will outline a feasibility study in which eye tracking was used to measure eye gaze metrics during a WMC task for adults with and without ADHD and machine learning algorithms were applied to generate a feature set unique to the ADHD diagnosis. The chapter will summarize the purpose, methods, results, and impact of this study