How does sentiment analysis work?
Sentiment analysis is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. It helps gauge public opinion, conduct market research, monitor brand or product reputation, analyze social media sentiment, and understand customer experiences. Using Skyl.ai AI Platform for NLP you can quickly build and deploy a high quality custom Sentiment Analysis ML model in hours.
Sentiment Analysis Industry use case:
MEDIA & PUBLISHING
Content Sentiment Analysis
Identifying the sentiments and preferences of your audience helps creators in curating the next content in a way that will appeal to the customers. AI helps in identifying these sentiments from a large base of feedback, reviews, and comments (even likes and dislikes) and tag the reactions using ML
Chat Sentiment Analysis
Understanding customer emotions is essential for the success of every business. Sentiment analysis allows companies to identify customer feedback toward products, brands or services in an online conversation. Chat sentiment analysis gives a fair idea about a company’s performance. It also highlights the aspects that need improvement like personalization, tone, efficiency, etc from customer’s point of view.
The internet provides a large platform for consumers to provide their opinions of a brand, its products, and services. It is vital for companies to pay attention to what is being said about their brands and what the sentiment of their user base is. AI can be used to analyze brand mentions on social media, review forums, surveys, and feedback on the company’s platform. Using NLP and sentiment analysis, companies can quickly assess the sentiments of their audience, take cues for improvements or affirmative action, and reach out to users to resolve cases of negative feedback. Sentiment analysis can also help companies find people who want to engage with their brands and take action to interact with them.
HR & RECRUITMENT
Corporates deal with dissatisfied and non-engaged workers occasionally, who are observed to show dips in productivity and efficiency. Employee feedback, corporate surveys, and appraisals to gather information about employee wellbeing can take a long time to process. AI can be used to gain insight into employee mindsets regularly and identify instances of dissatisfaction much quicker, through text sentiment analysis. Using NLP can help understand the underlying sentiment in employee feedback on company platforms, responses on surveys or during appraisals, and email conversations. AI can derive insights on how engaged and satisfied employees are with their work, colleagues, and the work environment.
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