YouTube sentiment analysis can be very valuable for brand insights. In this blog, we discuss how you can search, find, and retrieve insights from hundreds of YouTube videos with Repustate’s video analysis tool. We also broadly explain how sentiment analysis for YouTube comments is done.

2. How to do YouTube
Sentiment Analysis
for brand-insights
www.repustate.com

3. Sentiment Analysis For Youtube Videos
YouTube sentiment analysis can be very valuable for brand insights. In this
blog, we discuss how you can search, find, and retrieve insights from hundreds
of YouTube videos with Repustate’s video analysis tool. We also broadly
explain how sentiment analysis for YouTube comments is done.
Marketing strategy in the age of social media listening includes uncovering
brand and customer insights from YouTube videos. There are virtually millions
of feelings and opinions about brands on YouTube everyday, expressed by
people across all ages.

4. What is aspect-based sentiment analysis of
video reviews?
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● Aspect-based sentiment analysis breaks down a review into smaller segments, and studies
them for sentiment, thus enabling more detailed and accurate insights. Aspect-based
sentiment analysis can easily help distinguish which features of a product or service are liked
and which ones can be improved.
● Let’s see an example
● Went to Bar Chef last night and loved their drinks, especially the martinis, but the food was
horrible. My nachos tasted microwaved and the calamari was rubbery.
● This review needs to be analyzed at the aspect sentiment level, with further aspect insights
on Drinks (martinis), and Food are revealed through the aspects of nachos and calamari.

5. How does Repustate’s video analysis tool
perform YouTube sentiment analysis?
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Repustate’s video content analysis tool conducts aspect based
sentiment analysis on YouTube videos to deliver the most granular
brand insights. It uses advanced named entity recognition (NER) to
identify named entities in YouTube videos and classifies them into
predetermined categories. NER classifies company names, geo-
locations, things, and names of people who are mentioned in the
videos. These insights thus can be used to improve marketing
efforts, products, customer experience, or customer service.

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● Step 1: Collect & prepare video/audio/image/text data
Videos are converted into text using speech-to-text transcription models and run through
neural networks (NN) for audio content analysis. These NNs also discover caption overlays
in videos, and if detected, they read and extract text from it. They also employ image
detection for logos in background imagery.
All this video data, along with text data from the comments is collected and manually
edited to remove redundancies, punctuations, gifs, emojis, etc. It is then converted in a
machine-readable format (CSV, XLS, JSON) so it can be ingested into the machine
learning pipeline for training.

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● Step 2: Apply sentiment analysis
The data is run through the sentiment analysis API for opinion mining. It quickly
returns sentiment scores for each relevant topic, aspect, or entity ranging from -
1 for negative emotions, 0 for neutral feelings, and 1 for positive sentiment.
● Step 3: Visualize insights
Sentiment scores are presented in the form of visual reports consisting of
charts, graphs and tables through a sentiment visualization dashboard.

9. How is Sentiment analysis for YouTube
comments done?
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YouTube comments analysis can help with vital insights for media monitoring not
just for products and services but also for corporate and individuals in key
positions. Sentiment analysis for YouTube comments is done in broadly 3 steps:
● Step 1 - Scrapping & Preparing Youtube comments
● Step 2 - Running it through Sentiment analysis API
● Step 3 - Data visualization

10. Thank you!
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