# Sentiment Analysis on YouTube: A Brief Survey

arXiv: https://arxiv.org/abs/1511.09142 · HTML: https://mlsanity.com/paper/1511.09142/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2015.

## Built on by (most cited first, 5 of 5)

- [Transfer Learning for Low-Resource Sentiment Analysis](https://mlsanity.com/paper/2304.04703/) (2023)
- [Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis](https://mlsanity.com/paper/2403.01921/) (2024)
- [The Potential of Using Vision Videos for CrowdRE: Video Comments as a Source of Feedback](https://mlsanity.com/paper/2108.02076/) (2021)
- [Understanding Social Perception, Interactions, and Safety Aspects of Sidewalk Delivery Robots Using Sentiment Analysis](https://mlsanity.com/paper/2405.00688/) (2024)
- [CineXDrama: Relevance Detection and Sentiment Analysis of Bangla YouTube Comments on Movie-Drama using Transformers: Insights from Interpretability Tool](https://mlsanity.com/paper/2411.06548/) (2024)

## Abstract (arXiv)

Sentiment analysis or opinion mining is the field of study related to analyze opinions, sentiments, evaluations, attitudes, and emotions of users which they express on social media and other online resources. The revolution of social media sites has also attracted the users towards video sharing sites, such as YouTube. The online users express their opinions or sentiments on the videos that they watch on such sites. This paper presents a brief survey of techniques to analyze opinions posted by users about a particular video.