ArticleSensors (Basel, Switzerland)2022
Sentiment Analysis: An ERNIE-BiLSTM Approach to Bullet Screen Comments.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Sentiment trajectory modeling in mental health discussions via transformer-based analysis.Frontiers in artificial intelligence · 2026Article
- ERNIE-TextCNN: research on classification methods of Chinese news headlines in different situations.Scientific reports · 2025Article
- A weighted difference loss approach for enhancing multi-label classification.Scientific reports · 2025Article
- A novel approach for multiclass sentiment analysis on Chinese social media with ERNIE-MCBMA.Scientific reports · 2025Article
- Natural Language Processing (NLP): Identifying Linguistic Gender Bias in Electronic Medical Records (EMRs).Journal of patient experience · 2025Article
- Sentiment analysis of video danmakus based on MIBE-RoBERTa-FF-BiLSTM.Scientific reports · 2024Article
- Enhancing sentiment analysis of online comments: a novel approach integrating topic modeling and deep learning.PeerJ. Computer science · 2024Article
- Sentiment Impact of Public Health Agency communication Strategies on TikTok under COVID-19 Normalization: Deep Learning Exploration.Zeitschrift fur Gesundheitswissenschaften = Journal of public health · 2023Article
- Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis.Zeitschrift fur Gesundheitswissenschaften = Journal of public health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.