A Barrage Sentiment Analysis Scheme Based on Expression and Tone

Cui, Zongmin; Qiu, Quanlei; Yin, Chao; Yu, Jing; Wu, Zongda; Deng, Anyuan

doi:10.1109/access.2019.2957279

Cited by 10 publications

(7 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, they used a random forest algorithm to analyze bullet screen comments, predict the popularity of bullet screen videos, and construct an accurate bullet screen video recommendation system [ 19 ]. Cui et al [ 20 ] applied an unsupervised valence-arousal word approach to analyze emoticons and symbols in bullet screen comments and identify the sentiment categories of short texts.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Hsieh

Zeng

2022

Sensors

View full text Add to dashboard Cite

Sentiment analysis is one of the fields of affective computing, which detects and evaluates people’s psychological states and sentiments through text analysis. It is an important application of text mining technology and is widely used to analyze comments. Bullet screen videos have become a popular way for people to interact and communicate while watching online videos. Existing studies have focused on the form, content, and function of bullet screen comments, but few have examined bullet screen comments using natural language processing. Bullet screen comments are short text messages of different lengths and ambiguous emotional information, which makes it extremely challenging in natural language processing. Hence, it is important to understand how we can use the characteristics of bullet screen comments and sentiment analysis to understand the sentiments expressed and trends in bullet screen comments. This study poses the following research question: how can one analyze the sentiments ex-pressed in bullet screen comments accurately and effectively? This study mainly proposes an ERNIE-BiLSTM approach for sentiment analysis on bullet screen comments, which provides effective and innovative thinking for the sentiment analysis of bullet screen comments. The experimental results show that the ERNIE-BiLSTM approach has a higher accuracy rate, precision rate, recall rate, and F1-score than other methods.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

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

Hsieh

Zeng

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In the current field of information technology [28]- [31], information security is an extremely significant aspect that needs to be considered, and the protection of privacy information is an elementary need of the masses [32]- [34]. Outsourcing data security is one of our main research directions at present.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Protection Path Finding Supporting the Ranked Order on Encrypted Graph in Big Data Environment

Chen

Zhang

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Since data outsourcing in big data environment become popular and become a trend, large quantities of graph data are outsourced to the big data server for saving cost. As the big data server can not be fully reliable, people usually encrypt the graph data before they are outsourced to the big data platform for the privacy protection. The path finding is a frequently-used action and can be useful for production and living. The path finding supporting the ranked order is a more useful operation, and a user can obtain a ranked search result set. Because of the outsourced graph data being encrypted on the big data server, the path finding supporting the ranked order becomes a task with enough challenge. In this paper, we propose a solution to perform privacy-protection path finding supporting the ranked order on encrypted graph in big data environment (PPFR). Our research uses an encryption mechanism and a ranking strategy to achieve path finding supporting the ranked order. We formally analyze the security of our scheme. We demonstrate the efficiency of our proposed scheme on a real graph data set by experiment. INDEX TERMS path finding, encrypted graph, ranked order, searchable encryption, big data

show abstract

“…Aiming at the new features of danmakus, scholars have carried out explorations and attempts of sentiment analysis. Traditional danmaku sentiment analysis methods mostly utilize sentiment lexicon and machine learning models to judge the sentiment tendency of danmakus, Cui et al 15 expanded the traditional sentiment lexicon, innovatively proposed a sentiment lexicon based on emoticons and tone words, and set a single sentiment threshold as a threshold interval, which extends the scope of neutral danmakus; Hong et al 7 extended the scope of neutral danmakus by improving the traditional k-means clustering algorithm and introducing Dynamic Time Warping (DTW) to calculate the distance between user emotion distributions and clustered the danmaku data. In recent years, with the development of neural networks, more scholars apply deep learning methods in the danmaku sentiment analysis tasks.…”

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

A Barrage Sentiment Analysis Scheme Based on Expression and Tone

Cited by 10 publications

References 30 publications

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

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

Privacy-Protection Path Finding Supporting the Ranked Order on Encrypted Graph in Big Data Environment

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

Contact Info

Product

Resources

About