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With the rise of applications and fully digital user journeys the need to process feedback from users, clients and consumers have never been bigger. Sentiment analysis, also known as opinion meaning or emotion AI, is a combination of two techniques: machine learning and natural language processing. By using sentiment analysis, you classify the polarity of a text section in a document, a specific sentence or on the feature/aspect level, to determine whether the opinion expressed is positive, negative, or neutral.
Sentiment analysis can be used in several ways, from content marketing to finding out what your customers want/need. Today sentiment analysis has become a huge business by providing insights into how opinions are changing with the passage of time through social media language. The underlying algorithm learns what words people use to express themselves and with that knowledge provides an insight into sentiments towards that product or service.
This is done by categorizing phrases that are used on several social media channels. The sentiment analysis software uses machine learning to parse the phrases and will then try to match it with a category of positive, neutral, or negative. This sentiment is then used to give the user an insight into how people feel about what is being said. Sentiment analysis can be used in a few ways. For instance, you can use it to monitor your brand’s reputation, track customer satisfaction or product reviews and much more.
Some of the applications for sentiment analysis include:
- Brand monitoring
- Consumer research
- Employee sentiment
- Market research
- Product feedback
- Social listening
Sentiment analysis has become one of the most popular methods for extracting insights from social media data. The reason for this popularity is that social media posts often contain opinions that can be especially useful for businesses trying to understand how their products or services are perceived by the public.
One of the most common applications for sentiment analysis is brand monitoring. This involves tracking what people are saying about your brand on social media and other online platforms. This can be useful for identifying potential problems with your products or services and for gauging overall customer sentiment.
Another popular application for sentiment analysis is consumer research. This involves using sentiment analysis to understand how people feel about certain products or services. This can be useful for businesses that are looking to improve their offerings or for marketing teams that want to better understand what consumers are looking for.
Finally, sentiment analysis can also be used for employee sentiment tracking. This involves using sentiment analysis to understand how employees feel about their jobs and working conditions. This can be useful for businesses that want to invest in their people turnover rate and ensure they focus their people development on the areas that matter to the employees (versus on the ones that HR and management think matter to them). However, sentiment analysis is not just limited to social media data. In fact, sentiment analysis can be used on any type of text data, including news articles, reviews, blog posts, and even customer support tickets.
One of the most common applications for sentiment analysis is to track customer satisfaction. By monitoring sentiment over time, businesses can get an innovative idea of how their products or services are being received by the public. This information can then be used to make changes to improve customer satisfaction. Another common application for sentiment analysis is market research. By analyzing sentiment around certain topics or keywords, businesses can get a better understanding of what people are talking about in relation to their industry.
For more information about our API services check out the Ayfie API Services page and the specific one regarding the Sentiment Analysis API.