Sentiment analysis of video comments
How we want to extract sentiment of video comments
Hi, my name is Jérôme Vial. I am a student at the School of Engineering and Architecture of Fribourg in Switzerland. My study plan include two semester projects and for the second one I chose to work on a sentiment analysis application in collaboration with the HumanTech Institute.
A quick summary about the project
This project is part of the Coléarnis project. Coléarnis is a platform created in collaboration with the School of Engineering of Neuchâtel. Coléarnis allows enterprise to create video in order to pass the knowledge from experienced employees to the other employees. Employees can then react to the video through comments and that’s where my project take place.
What are the project goals ?
This sentiment extraction application has many uses, but the main goal is to create a pipeline to extract the sentiment of video comments written in french. Sentiment analysis in french and on short texts are the two main constraints for this project.
During the project I reached theses two goals, it means I figured out how to extract sentiment on video comments written in french. To reach the goals I did research on existing technologies and I tested each of them. I figured out that using Google CNL and PyFeel would be the best options as Google CNL has strong results and PyFeel is developed especially for the french language.
Which technology and why ?
A big part of the project is research. I needed to find existing technologies that are working on short texts and with french texts. I found many technologies to perform sentiment analysis:
- Google CNL
- PyFeel
- Amazon Comprehend
- Azure Cognitive
- IBM Natural Language Understanding
All theses technologies are working with french texts, but after I performed some tests and compared them with each other I chose to use Google CNL and PyFeel. The main reasons are the fact that Google CNL has the best performances overall and the fact that PyFeel is a local library, which means no network data transfer.
Emoji’s importance in sentiment analysis
Since the project is to extract sentiment of video comments, I figured out that many comments contain emojis. In fact, those little yellow faces born in 1999 became the standard in text writing and in our way of expressing ourself. In 2019, 92% of the people use emojis! Emojis are really important in comments, they allow the user to express his feeling quickly and without having to write a lot of text. That’s why in the pipeline I added the library called Emosent to analyse emojis as well.
The pipeline, how does it work ?
The project has the shape of a pipeline, it means each part has a specific task. To begin, the first part is the pre-processing. It’s the part where the data are collected, cleaned and put in shape in order to facilitate the sentiment extraction.
The second part is the processing, it’s the part where the three technologies (Google CNL, PyFeel and Emosent) work together to extract the sentiment. I chose to extract the sentiment using Google CNL and PyFeel together to enforce the result.
After the extraction is done there is the last part, the analyzing part where I check the results and prepare the output. The output is mainly constituted of statistics like “80% of the users had a good feeling during this video.”.
What about the future ?
This project is an open door to many other projects. Even if the main goal is to extract sentiment of video comments, we can easily transform it into something else.
We could use this project and tune it to be able to create an app where the user drops a dataset, then the pipeline extracts the sentiments and finally the user manually check if the extraction is correct or not. Possibilities like this allows us to include human-in-the-loop process and also to upgrade our extractor to perform better.
I also recommend to create a real dataset that contains comments from the project platform. For this project I extracted comments on YouTube and I used existing dataset, but they poorly represent the comments that would be on the Coléarnis platform.
A self-reflection about my work
At the end of the schedule, I am satisfied because I reached all of my milestones in time and I reached the main goal with providing an application to extract sentiments of video comments written in french. I learned a lot during this project and it created an interest for this domain.
The main project is far to be done, but the purpose of this project was to do some research and test in order to help the development of the Coléarnis platform.
Thank you for reading my article and I hope I created interest in sentiment analysis !
