Sentiment Analysis: An ERNIE-BiLSTM Approach to Bullet Screen Comments

Hsieh, Yen-Hao; Zeng, Xin

doi:10.3390/s22145223

Cited by 14 publications

(8 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Second, regarding the rich Chinese corpus, ERNIE has added Chinese corpora, such as the Baidu encyclopedia and Baidu news, to enhance its effectiveness on Chinese tasks. Finally, regarding dialog embedding (Hsieh and Zeng 2022 ), ERNIE’s training corpus introduces knowledge from multiple data sources, such as news and information and web forum conversation data. This kind of learning for conversation data is an essential method of semantic representation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis

Che

Wang²,

Zhang³

et al. 2023

J Public Health (Berl.)

View full text Add to dashboard Cite

Aim This study explored the influence of daily new case videos posted by public health agencies (PHAs) on TikTok in the context of COVID-19 normalization, as well as public sentiment and concerns. Five different stages were used, based on the Crisis and Emergency Risk Communication model, amidst the 2022 Shanghai lockdown. Subject and Methods After dividing the duration of the 2022 Shanghai lockdown into stages, we crawled all the user comments of videos posted by Healthy China on TikTok with the theme of daily new cases based on these five stages. Third, we constructed the pre-training model, ERNIE, to classify the sentiment of user comments. Finally, we performed semantic network analyses based on the sentiment classification results. Results First, the high cost of fighting the epidemic during the 2022 Shanghai lockdown was why ordinary people were reluctant to cooperate with the anti-epidemic policy in the pre-crisis stage. Second, Shanghai unilaterally revised the definition of asymptomatic patients led to an escalation of risk levels and control conditions in other regions, ultimately affecting the lives and work of ordinary people in the area during the initial event stage. Third, the public reported specific details that affected their lives due to the long-term resistance to the epidemic in the maintenance stage. Fourth, the public became bored with videos regarding daily new cases in the resolution stage. Finally, the main reason for the negative public sentiment was that the local government did not follow the central government’s anti-epidemic policy. Conclusion Our results suggest that the methodology used in this study is feasible. Furthermore, our findings will help the Chinese government or PHAs improve the possible behaviors that displease the public in the anti-epidemic process.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Finally, regarding dialog embedding (Hsieh and Zeng 2022 ), ERNIE’s training corpus introduces knowledge from multiple data sources, such as news and information and web forum conversation data. This kind of learning for conversation data is an essential method of semantic representation.…”

Section: Introductionmentioning

confidence: 99%

Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis

Che

Wang²,

Zhang³

et al. 2023

J Public Health (Berl.)

View full text Add to dashboard Cite

show abstract

“…In recent years, with the development of neural networks, more scholars apply deep learning methods in the danmaku sentiment analysis tasks. Zhao et al 16 proposed a multi-head attention convolutional neural network (MH-ACNN) to analyze the sentiment of danmakus for their short content and incomplete contextual information; Hsieh and Zeng 17 used ERNIE to characterize danmaku texts and then used BiLSTM to analyze danmakus.…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis of video danmakus based on MIBE-RoBERTa-FF-BiLSTM

Zhao,

Liu,

Wang

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Danmakus are user-generated comments that overlay on videos, enabling real-time interactions between viewers and video content. The emotional orientation of danmakus can reflect the attitudes and opinions of viewers on video segments, which can help video platforms optimize video content recommendation and evaluate users’ abnormal emotion levels. Aiming at the problems of low transferability of traditional sentiment analysis methods in the danmaku domain, low accuracy of danmaku text segmentation, poor consistency of sentiment annotation, and insufficient semantic feature extraction, this paper proposes a video danmaku sentiment analysis method based on MIBE-RoBERTa-FF-BiLSTM. This paper constructs a “Bilibili Must-Watch List and Top Video Danmaku Sentiment Dataset” by ourselves, covering 10,000 positive and negative sentiment danmaku texts of 18 themes. A new word recognition algorithm based on mutual information (MI) and branch entropy (BE) is used to discover 2610 irregular network popular new words from trigrams to heptagrams in the dataset, forming a domain lexicon. The Maslow’s hierarchy of needs theory is applied to guide the consistent sentiment annotation. The domain lexicon is integrated into the feature fusion layer of the RoBERTa-FF-BiLSTM model to fully learn the semantic features of word information, character information, and context information of danmaku texts and perform sentiment classification. Comparative experiments on the dataset show that the model proposed in this paper has the best comprehensive performance among the mainstream models for video danmaku text sentiment classification, with an F1 value of 94.06%, and its accuracy and robustness are also better than other models. The limitations of this paper are that the construction of the domain lexicon still requires manual participation and review, the semantic information of danmaku video content and the positive case preference are ignored.

show abstract

“…The GoogleNet, a 22-layers deep Convolutional Neural Network (CNN), developed by a group of researchers of Google [8], can extract the image features the way the human visual cortex does [9]. And the Bidirectional Long Sort Term Memory (BiLSTM) can learn these features to classify YouTube videos [10], identify human sentiments [11], and recognize human activities [12]. That means a combination of these two networks exhibits the potential to extract video features from video streams and identify human activities from them.…”

Section: Introductionmentioning

confidence: 99%

A Novel Front Door Security (FDS) Algorithm Using GoogleNet-BiLSTM Hybridization

Paula¹,

Faruqui

Mahmud

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Security has always been a significant concern since the dawn of human civilization. That is why we build houses to keep ourselves and our belongings safe. And we do not hesitate to spend a lot on front-door locks and install CCTV cameras to monitor security threats. This paper presents an innovative automatic Front Door Security (FDS) algorithm that uses Human Activity Recognition (HAR) to detect four different security threats at the front door from a real-time video feed with 73.18% accuracy. The activities are recognized using an innovative combination of GoogleNet-BiLSTM hybrid network. This network receives the video feed from the CCTV camera and classifies the activities. The proposed algorithm uses this classification to alert any attempts to break the door by kicking, punching, or hitting. Furthermore, the proposed FDS algorithm is effective in detecting gun violence at the front door, which further strengthens security. This Human Activity Recognition (HAR)-based novel FDS algorithm demonstrates the potential of ensuring better safety with 71.49% precision, 68.2% recall, and an F1-score of 0.65.

show abstract

Sentiment Analysis: An ERNIE-BiLSTM Approach to Bullet Screen Comments

Cited by 14 publications

References 41 publications

Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis

Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis

Sentiment analysis of video danmakus based on MIBE-RoBERTa-FF-BiLSTM

A Novel Front Door Security (FDS) Algorithm Using GoogleNet-BiLSTM Hybridization

Contact Info

Product

Resources

About