• Corpus ID: 241035366

Classifying YouTube Comments Based on Sentiment and Type of Sentence

@article{Pokharel2021ClassifyingYC,
  title={Classifying YouTube Comments Based on Sentiment and Type of Sentence},
  author={Rhitabrat Pokharel and Dixit Bhatta},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.01908}
}
As a YouTube channel grows, each video can potentially collect enormous amounts of comments that provide direct feedback from the viewers. These comments are a major means of understanding viewer expectations and improving channel engagement. However, the comments only represent a general collection of user opinions about the channel and the content. Many comments are poorly constructed, trivial, and have improper spellings and grammatical errors. As a result, it is a tedious job to identify… 

References

SHOWING 1-10 OF 24 REFERENCES
How useful are your comments?: analyzing and predicting youtube comments and comment ratings
TLDR
An in-depth study of commenting and comment rating behavior on a sample of more than 6 million comments on 67,000 YouTube videos for which dependencies between comments, views, comment ratings and topic categories are analyzed.
Polarity Trend Analysis of Public Sentiment on YouTube
TLDR
It is demonstrated that an analysis of the sentiments of the YouTube comments to identify their trends, seasonality and forecasts can provide a clear picture of the influence of real-world events on user sentiments.
A classification scheme for content analyses of YouTube video comments
TLDR
This study seeks to examine and categorise the types of comments created by YouTube users to highlight the various ways in which this interactive feature has been employed as a means of communication and self-expression.
Retrieving YouTube video by sentiment analysis on user comment
TLDR
A Natural Language processing (NLP) based sentiment analysis approach on user comments helps to find out the most relevant and popular video of YouTube according to the search.
Learning Word Vectors for Sentiment Analysis
TLDR
This work presents a model that uses a mix of unsupervised and supervised techniques to learn word vectors capturing semantic term--document information as well as rich sentiment content, and finds it out-performs several previously introduced methods for sentiment classification.
Analyzing User Comments on YouTube Coding Tutorial Videos
TLDR
This paper analyzes user comments on YouTube coding tutorial videos to help content creators to effectively understand the needs and concerns of their viewers, thus respond faster to these concerns and deliver higher-quality content.
Sentiment analysis of students' comment using lexicon based approach
TLDR
By analyzing the sentiment information including intensifier words extracting from students' feedback, this system is able to determine opinion result of teachers, describing the level of positive or negative opinions.
Lexicon-Based Methods for Sentiment Analysis
TLDR
The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation, and is applied to the polarity classification task.
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions
TLDR
A novel machine learning framework based on recursive autoencoders for sentence-level prediction of sentiment label distributions that outperform other state-of-the-art approaches on commonly used datasets, without using any pre-defined sentiment lexica or polarity shifting rules.
Experiments with Sentence Classification
TLDR
A set of experiments involving sentence classification are presented, addressing issues of representation and feature selection, and the findings are compared with similar results from work on the more general text classification task.
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