How To Do Sentiment Analysis on YouTube Comments

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AI-enabled business-driven sentiment analysis and text analytics like YouTube comments analysis has taken the business world by storm. Every brand, every organization, eager to increase its efficiency, productivity, and growth is exploring ways to use sentiment analysis with social media listening. At the same time, many brands shy away from exploring AI-powered platforms because the task seems intimidating and inundated with hassles. Nothing could be further than the truth. In fact, technology gives us customer insights that are more accurate and more targeted than manual analysis.

It all begins when you identify what exactly you want to better about your business, and who your customers are. Then you find the right online channel to ensure that that’s where your audience expresses themselves most optimally. For example, many beauty brands conduct analysis on YouTube comments for this purpose. Once you’ve established the source, all you need is the right sentiment analysis and text analytics solution. It does all the work for you and gives you all the customer insights you need for actionable growth decisions.

In this particular article, our aim is to help you utilize Repustate IQ’s free trial for YouTube comments analysis. You will see how straightforward and simple it is to use. So let’s get started.

Social Media Sentiment Analysis

All brands, regardless of industry, need social media sentiment analysis to stay ahead of the competition. Noticeably, brands include not just businesses but individuals as well. This is because social listening tools help you understand trends that develop due to people’s opinions. Organizations use these insights to use audience opinion in their favour, or to subtly tilt them in their direction. Social media sentiment analysis is used as much in political campaigns (eg. Brexit) and public health policies like we are witnessing with Covid19, as in retail like H&M has done.

Organizations can analyze comments from Facebook and TikTok, just like YouTube comments analysis with an intelligent sentiment analysis API for in-depth insights on trending data. In fact, user-generated videos on TikTok and YouTube, comments from Xing and Twitter, all can be gathered and processed to understand the general sentiment around a variety of topics. This is vital, especially for market research. And yet, without the trouble of depending on people to fill out a form for you.

That’s why AI-based technologies are so useful for brands to conduct sentiment analysis on YouTube comments or from any other source, really. You can easily depend on a text analytics API to glean all the information you need and give you the insights you require for strategic business growth without manual dependencies and the errors that come with it.

So let’s read further and find out how you can examine and study comments from a social media channel – in this case- YouTube comments analysis and harness vital information for brand amplification.

How To Do Sentiment Analysis on YouTube Comments Yourself?

There are three steps involved if you want to take advantage of social listening benefits without the use of automatic data-fetching and sentiment analysis.

They are as follow:

Step 1: YouTube Comments Data Preparation

To begin YouTube comments analysis of the video of your choice, you need to ensure that the data you are gathering is cleaned and prepped for the machine learning platform for sentiment analysis. You can use web scraping tools like Import.io, ParseHub, and Dexi.io to collect the comments. If you don’t have access to the YouTube API, you can use yt-comment-scraper – npm. All this gathered data must be in a .csv or excel format so that it is compatible with the machine learning API you will be using.

Once you have collected the data you want, you need to clean it up by manually removing non-text items like emojis, special characters, redundant words, URLs, etc. This step is very important because the quality of your analysis depends on the quality of your data.

Step 2: Data Processing via a Sentiment Analysis API

You now need to run the data that you have collected in the .csv file through a text analytics and emotion mining platform for sentiment analysis on YouTube comments. The software will first conduct text analysis on your data through its named entity recognition capability and extract entities like brand mentions, people, locations, and the like.

The natural language processing task will simultaneously recognize and extract key aspects and features depending on the topic of the video. For example, if the video is about a hotel, it will identify aspects like cleanliness, convenience, rooms, restaurant, etc. The API will ultimately analyze all these aspects, themes, and entities for the sentiment. It will mine the emotions expressed about every single one of them and assign an aggregate score to each one individually.

Step 3: Insights Visualization

The processed data will now be shown on a customer experience dashboard so that you can see all the comparisons and inputs in the form of graphs and pie charts. You can see things like the total number of comments, how many of them are negative, how many positive. You can see aspect and emotion co-occurrence, which will show you which emotions correspond to which aspects.

For example, how high or low is the emotion “happiness” with an aspect like work satisfaction.

All these insights when put together give you a holistic view of sentiment analysis on YouTube comments so that you can see patterns and trends in the data. In a broader scheme of things, this bird’s eye view is very important when dealing with online reputation management, competitive insights, and brand protection and amplification.

Also Read: TikTok for Business

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