How to do Youtube Sentiment Analysis
Overview With YouTube being the most popular social media platform having 2 billion monthly active users and the second one after Google to be the world's second largest search engine. Yet, brands wonder why they need opinion mining and social listening on YouTube. YouTube users continue to grow daily, and YouTube comments can provide great information and actionable insights for any brand. All those unwanted opinions and open customer feedback are free to take when you have the right tools in place.
What is Social Media Sentiment Analysis? With the growth of technology and social media platforms such as Facebook, YouTube, Twitter, customers are actively participating in providing feedbacks on the company's services and products. These reviews are trusted by the customers globally in order to associate with the brand. These feedbacks serve as a third-party validation tool to build user trust in the brand. Hence, it is vital to understand customer feedback and sentiment analysis can be an augment tool for any organization. One of the natural language processing techniques is Sentiment Analysis. It specifies the sensibility behind the texts, i.e. tweets, youtube comments, movie reviews, any incoming message, etc.
What is aspect-based sentiment analysis of video reviews? Aspect-based Sentiment Analysis helps in improving the business by knowing the features they are looking in the product to improve according to customer's feedback and make it their bestseller product. It recognizes the aspects in the provided review about a product and encounters the element's sentiment class. YouTube sentiment analysis decides which categories are being mentioned and then computes each category's sentiment. When compiled in totality across many reviews, the strengths and shortcomings of a business's product or services surface quickly, and actionable insights become apparent instantly.
Advantages Of Aspect-Based Sentiment Analysis Feature analysis Aspect-based sentiment analysis divides big data into blocks that are further examined by removing features of the subject from it even if it's not mentioned explicitly. Emotion aspect co-occurrence This feature tells you which emotion overlaps with which element of your product or service based on the voice of the customer data. Semantic clustering Semantic clustering is one of the crucial features of aspect-based sentiment analysis. Data is processed through a sentiment analysis API and experiences text analytics in the first step.
How do video analysis tools perform YouTube sentiment analysis? The video content analysis tool conducts aspect-based sentiment analysis on YouTube videos to provide the most granulated brand insights. It uses state-of-the-art named entity recognition (NER) to recognize named entities in YouTube videos and organizes them into predetermined categories. These insights thus can be used to enhance customer experience, marketing efforts, products, or customer service. Let us understand the steps involved in the process: ● ● ●
Step 1: Collect & prepare video/audio/image/text data Step 2: Apply sentiment analysis Step 3: Visualize insights
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