Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions

Bhende, Manisha; Thakare, Anuradha; Pant, Bhasker; Singhal, Piyush; Shinde, Swati; Dugbakie, Betty Nokobi

doi:10.1155/2022/5075277

Cited by 7 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the outstanding performance of BERT in Section 3.2.1, we also selected BERT as our classifier. We fine-tuned BERT with RoBERTa-wwm-ext-large [25] based on the complexity of multi-class classification (F1 score: 66.13%, with the similar level of performance to other works in multiclass classification of emotions on social media [13,29,65,78]). Then, we applied the classification model to 31, 714 potentially emotional posts.…”

Section: Emotion Category Identification and Classificationmentioning

confidence: 99%

Understanding Emotional Disclosure via Diary-keeping in Quarantine on Social Media

Deng,

He,

2023

Proceedings of the Eleventh International Symposium of Chinese CHI

View full text Add to dashboard Cite

Quarantine is a widely-adopted measure during health crises caused by highly-contagious diseases like COVID-19, yet it poses critical challenges to public mental health. Given this context, emotional disclosure on social media in the form of keeping a diary emerges as a popular way for individuals to express emotions and record their mental health status. However, the exploration of emotional disclosure via diary-keeping on social media during quarantine is underexplored, understanding which could be beneficial to facilitate emotional connections and enlighten health intervention measures. Focusing on this particular form of self-disclosure, this work proposes a quantitative approach to figure out the prevalence and changing patterns of emotional disclosure during quarantine, and the possible factors contributing to the negative emotions. We collected 58, 796 posts with the "Quarantine Diary" keyword on Weibo, a popular social media website in China. Through text classification, we capture diverse emotion categories that characterize public emotion disclosure during quarantine, such as annoyed, anxious, boring, happy, hopeful and appreciative. Based on temporal analysis, we uncover the changing patterns of emotional disclosure from long-term perspectives and period-based perspectives (e.g., the gradual decline of all negative emotions and the upsurge of the annoyed emotion near the end of quarantine). Leveraging topic modeling, we also encapsulate the possible influencing factors of negative emotions, such as freedom restriction and solitude, and uncertainty of infection and supply. We reflect on how our findings could deepen the understanding of mental health on social media and further provide practical and design implications to mitigate mental health issues during quarantine. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI.

show abstract

Section: Emotion Category Identification and Classificationmentioning

confidence: 99%

Understanding Emotional Disclosure via Diary-keeping in Quarantine on Social Media

Deng,

He,

2023

Proceedings of the Eleventh International Symposium of Chinese CHI

View full text Add to dashboard Cite

show abstract

“…This adjustment accounts for most phrases, for example, in the statement "I like this laptop," numerous adjectives that aim at several nous are also dealt with. BiLSTM provides result positive after the implementation of the phrase [32] [33]. The general equation of BiLSTM for understanding the output is as: Text semantic Attention (Text Vectors Formation) Certain words in the text are crucial while conveying the author's intended tone.…”

Section: Feature Set Extractionmentioning

confidence: 99%

Multus Medium Opinion Mining (MMOM): A Novel Hybrid Deep Learning Approach for Comprehensive Product Review Analysis

Kanwal¹,

Nawaz²,

Mustafa³

et al. 2023

Preprint

View full text Add to dashboard Cite

Advancements in technology have revolutionized communication on social media platforms, where reviews and comments greatly influence product selection. Current opinion mining methods predominantly focus on textual content, overlooking the rich information within customer-posted images, termed here as Multus-Medium. This research introduces an innovative deep learning approach, Multus Medium Opinion Mining (MMOM), capitalizing on both text and image data for comprehensive product review analysis. MMOM employs an integrated model of Bidirectional Long Short-Term Memory (BiLSTM) and embedded Convolutional Neural Network (CNN), incorporating architectures of GoogleNet and VGGNet, thus enabling efficient extraction and fusion of textual and visual features. This synergistic approach enables data collection, preprocessing, feature extraction, fusion strategy-based feature vector generation, and subsequent product recommendation. Performance evaluation on two diverse real-world datasets name as “flicker8k” and “t4sa”. These datasets show substantial improvement over existing methods. MMOM outperforms standard benchmark models, achieving an accuracy, F1 score and ROC over fliker8k are 90.38%, 88.75% and 93.08%, whereas for twitter dataset are 88.54%, 86.34%, and 92.26% respectively, the accuracy of purposed model is 7.34% and 9.54% higher than the other two mentioned techniques. These statistics highlight the robustness and applicability of MMOM across various domains. The compelling results underscore the potential of MMOM, providing a more holistic and precise approach to opinion mining in the era of social media product reviews. This research, product recommendation helps the customer to make purchasing decision. Last but not the least, the purposed scheme can further be expanded in any other sentiment task like hospital recommendation system, crop farming recommendation and medical diagnostic system etc.

show abstract

“…In Woldeab, D, (2019), they conducted the study by sampling data on 631 students, which focuses on how they face online exams. In this they try to evaluate the results in the context of students by evaluating the anxiety and difficulty level faced by students during the online examinations [7,13].…”

Section: Literature Reviewmentioning

confidence: 99%

Online or Offline Exam in Covid-19 Pandemic: Analysis and Assessment in the Context of Students/ Staff and its Impacts

2022

International Journal of Research Science and Management

View full text Add to dashboard Cite

Covid-19 pandemic has affected the overall structure of the education system, it forced them to change the traditional methods we use for teaching and also the methods we use to analyze them i.e. offline exam. These changes are so sudden that we don't have proper time to plan them; we have to act instantly and shift everything in online mode, even the paper exams. Due to this sudden transition not only colleges and schools have to face problems, students also have to face lots of problems. In this paper, we try to cover all the problems which are faced by the students and college administration during online exams by conducting a small survey. For conducting a survey we design

show abstract

Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions

Cited by 7 publications

References 29 publications

Understanding Emotional Disclosure via Diary-keeping in Quarantine on Social Media

Understanding Emotional Disclosure via Diary-keeping in Quarantine on Social Media

Multus Medium Opinion Mining (MMOM): A Novel Hybrid Deep Learning Approach for Comprehensive Product Review Analysis

Online or Offline Exam in Covid-19 Pandemic: Analysis and Assessment in the Context of Students/ Staff and its Impacts

Contact Info

Product

Resources

About