How to do YouTube Sentiment Analysis for brand-insights www.repustate.com
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.
Sentiment Analysis For Youtube Videos 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.
What is aspect-based sentiment analysis of video reviews? ●
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.
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Let’s see an example
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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.
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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.
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, 02 geo-locations, things, and names of people who are mentioned in the videos. These insights thus can be used to improve marketing efforts,03 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
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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
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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.
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Step 3: Visualize insights
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Sentiment scores are presented in the form of visual reports consisting of charts, graphs and tables through a sentiment visualization dashboard.
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How is Sentiment analysis for YouTube comments done? 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:
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Step 1 - Scrapping & Preparing Youtube comments
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Step 2 - Running it through Sentiment analysis API
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Step 3 - Data visualization
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