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How To Do Sentiment Analysis on YouTube Comments With Repustate IQ’s Free Trial

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.

What Is The Youtube Comments Analysis Three-Step Process With Repustate IQ?

The three steps for sentiment analysis on YouTube comments include registering for the free trial, fetching the data of the video you want to analyze, and voila! results of the sentiment analysis await you on the customer dashboard. It’s that simple. Let’s look at the steps a little bit more in detail.

Step 1: Create Your Free Trial Account

Once you’ve done that, you can access the solution for YouTube comments analysis. The platform works with a number of sources, so to fetch your data for analysis, choose the platform (here we choose YouTube) and select the video from which you want the comments gathered.

Step 2: Submit Video URL

Specify the URL and submit for sentiment analysis on YouTube comments. The API will begin the fetching process and download all of the information. The software will process the entire data by extracting entities and running named entity recognition (NER) and natural language processing (NLP) tasks. It will eventually process the extracted entities, aspects, and themes from the data and assign sentiment scores to them.

Step 3: Visualize Data

All the insights from the YouTube comments analysis will now appear on the sentiment analysis dashboard in the form of charts and statistics. You will be able to see details like the percentage of negative and positive sentiment for each topic, and more.

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, ParseHub, and 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.

Discover More: YouTube Video Analysis

Benefits of using Repustate

Repustate IQ brings you the unique capabilities of video content analysis (VCA), multilingual data analysis using native language instead of translations, and named entity recognition (NER) that even beats giants like Google and Amazon. It is important to note that VCA is not comments analysis, but the analysis of the video itself for brand insights. This means that you can get a complete picture of the video data that you want to analyze instead of just Youtube comments analysis, or TikTok/IGTV comments analysis.

Languages can be an issue for many platforms on the market even though on the surface they all claim to be available for 30 to 60 languages. But the truth is that all of them use translations, which in reality give very inaccurate data due to the difference in language lexicology, syntaxes, and grammatical constructs. Repustate IQ has individual speech taggers for each of the 23 languages it supports. It never uses translations, which is the secret behind its high-precision results. Added to that, is its enviable NER capability.

As giants like L’Oreal, CeraVe, Dunkin Donuts and so many like them take to TikTok and other video-based channels, you too need a sentiment analyser that can scale limitlessly, while giving precise insights from competitor brand videos and comments.

So stay a step ahead of the turn. Use AI-powered sentiment analysis to reduce your expenses, explore shopping trends, increase your brand awareness and reputation security, and more importantly, reimagine your marketing and advertising efforts. Let AI do the hard work for you.

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