Document-Level Sentiment Analysis through Incorporating Prior Domain Knowledge into Logistic Regression

Wasi, Nesar Ahmad; Abulaish, Muhammad

doi:10.1109/wiiat50758.2020.00148

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Thus, by taking the exponential of β1, we obtain the odds ratio corresponding to a 1-unit change in X. The logistic regression model has been used in many social media-based sentiment analysis studies [65][66][67].…”

Section: Logistic Regressionmentioning

confidence: 99%

Social Media Users’ Perceptions of a Wearable Mixed Reality Headset During the COVID-19 Pandemic: Aspect-Based Sentiment Analysis

Jeong¹,

Bayro²,

Umesh³

et al. 2022

JMIR Serious Games

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Background Mixed reality (MR) devices provide real-time environments for physical-digital interactions across many domains. Owing to the unprecedented COVID-19 pandemic, MR technologies have supported many new use cases in the health care industry, enabling social distancing practices to minimize the risk of contact and transmission. Despite their novelty and increasing popularity, public evaluations are sparse and often rely on social interactions among users, developers, researchers, and potential buyers. Objective The purpose of this study is to use aspect-based sentiment analysis to explore changes in sentiment during the onset of the COVID-19 pandemic as new use cases emerged in the health care industry; to characterize net insights for MR developers, researchers, and users; and to analyze the features of HoloLens 2 (Microsoft Corporation) that are helpful for certain fields and purposes. Methods To investigate the user sentiment, we collected 8492 tweets on a wearable MR headset, HoloLens 2, during the initial 10 months since its release in late 2019, coinciding with the onset of the pandemic. Human annotators rated the individual tweets as positive, negative, neutral, or inconclusive. Furthermore, by hiring an interannotator to ensure agreements between the annotators, we used various word vector representations to measure the impact of specific words on sentiment ratings. Following the sentiment classification for each tweet, we trained a model for sentiment analysis via supervised learning. Results The results of our sentiment analysis showed that the bag-of-words tokenizing method using a random forest supervised learning approach produced the highest accuracy of the test set at 81.29%. Furthermore, the results showed an apparent change in sentiment during the COVID-19 pandemic period. During the onset of the pandemic, consumer goods were severely affected, which aligns with a drop in both positive and negative sentiment. Following this, there is a sudden spike in positive sentiment, hypothesized to be caused by the new use cases of the device in health care education and training. This pandemic also aligns with drastic changes in the increased number of practical insights for MR developers, researchers, and users and positive net sentiments toward the HoloLens 2 characteristics. Conclusions Our approach suggests a simple yet effective way to survey public opinion about new hardware devices quickly. The findings of this study contribute to a holistic understanding of public perception and acceptance of MR technologies during the COVID-19 pandemic and highlight several new implementations of HoloLens 2 in health care. We hope that these findings will inspire new use cases and technological features.

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Section: Logistic Regressionmentioning

confidence: 99%

Social Media Users’ Perceptions of a Wearable Mixed Reality Headset During the COVID-19 Pandemic: Aspect-Based Sentiment Analysis

Jeong¹,

Bayro²,

Umesh³

et al. 2022

JMIR Serious Games

View full text Add to dashboard Cite

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An Unseen Features Enhanced Text Classification Approach

Wasi

Abulaish

2023

2023 International Joint Conference on Neural Networks (IJCNN)

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Data and domain knowledge dual‐driven artificial intelligence: Survey, applications, and challenges

Nie,

Jiang,

et al. 2023

Expert Systems

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At present, the mainstream mode of machine learning algorithms is the data‐driven method, which mainly relies on the self‐learning ability of deep neural networks and continuously evolving models in data‐driven training. However, the pure data‐driven method has some critical problems, such as high data collection cost, poor interpretability and easy to be be disturbed by noise. Although the knowledge‐driven method has high stability, it lacks self‐learning and evolution ability in the face of comprehensive and complex problems. In recent years, the convergence of data and domain knowledge has combined the advantages of both learning paradigms. One typical way is to embed domain knowledge into the data‐driven model to improve the interpretability of the model, and then use the self‐learning ability of the data‐driven model to explore knowledge, and continuously iterate the domain knowledge to form a closed loop. The data‐knowledge dual‐driven methods have brought transformative innovations in machine learning. This review first introduced the advantages and necessity of the data‐knowledge dual‐driven model in the field of artificial intelligence. Then, the applications of the data‐knowledge dual‐driven model in the smart marine field were introduced. Finally, the challenges and trends of the data‐knowledge dual‐driven artificial intelligence are anticipated.

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Document-Level Sentiment Analysis through Incorporating Prior Domain Knowledge into Logistic Regression

Cited by 3 publications

References 14 publications

Social Media Users’ Perceptions of a Wearable Mixed Reality Headset During the COVID-19 Pandemic: Aspect-Based Sentiment Analysis

Social Media Users’ Perceptions of a Wearable Mixed Reality Headset During the COVID-19 Pandemic: Aspect-Based Sentiment Analysis

An Unseen Features Enhanced Text Classification Approach

Data and domain knowledge dual‐driven artificial intelligence: Survey, applications, and challenges

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