Sentiment Analysis is a term used to describe the processes and techniques that organizations use to gather information regarding how their customers feel about a specific product or service. Some people also refer to this process as Opinion Mining.
Sentiment Analysis looks at the feelings behind the phrases and expressions people use when leaving feedback. It considers their attitudes, emotions, opinions, and thoughts when creating and utilizing tools of Natural Language Processing (NLP). This tool attempts to create and understand the natural language that people use when buying and interacting with products and services.
Along with NLP, Sentiment Analysis also relies on Machine Learning to enable businesses to consider things beyond comments, likes, and shares on blog posts and other advertising campaigns. At Call Sumo, we understand that you likely have a lot of questions about Sentiment Analysis. We will describe the tools and methods of Sentiment Analysis as well as why so many people now use it so extensively.
Sentiment Analysis and Math
Typically, you can read a post and understand whether its author feels positive or negative about the topic. This can only happed if you have a good understanding of the English language, which a computer doesn’t have. Since it doesn’t naturally understand how spoken language works, it’s necessary to use a mathematical equation because that’s how computers operate. There is simply no way for a computer to determine whether a person intended to convey anger, joy, frustration, or other emotions if it doesn’t have context for the words.
By employing NLP, Sentiment Analysis can solve this issue. It reads and recognizes the most useful phrases and keywords contained in a document. Over time, this aids the algorithm in classifying the emotional intent of the writer of the document. Programmers and data scientists write applications and feed documents into the algorithm determined by the machine. It then maintains the results in a manner that is most useful for companies to understand and use.
One of the simplest techniques used by Sentiment Analysis is keyword spotting. The machine scans the input document for words that obviously express a positive or negative emotion. Happy and disappointed are two prime examples. Of the various algorithms, each one has a library containing phrases and words scored as negative, neutral, or positive.
This technique isn’t without its drawbacks. One is that it can’t differentiate when one user expresses two very different emotions. The solution was to create an algorithm that reads words such as and or but as a clue of the various types of sentiment expressed. Each part of the sentence receives a separate score, known formally as Binary Sentiment Analysis.
Machine learning algorithms can never be perfect and the same is true of sentiment analysis. Because language is complex, the best we can hope for is approximately 80 percent.
Why Use Sentiment Analysis?
The Internet is constantly shifting, which means that brands have had to make use of Sentiment Analysis to keep up. It’s the best way for them to understand customer expectations and experiences. Social media listening is another tools companies can use from any of their domains to understand customer concerns. This helps them improve customer service.
It may even be possible to use Sentiment Analysis to analyze Twitter data to improve stock market predictions. Research indicates that social media and news articles can have a much larger impact on the stock market than anyone ever realized. Positive news has proven to tie into a big increase in stock prices, even if it’s only for a short time. By the same token, negative news can make the prices decrease for a longer time.
Here’s what you can expect from Sentiment Analysis:
- Advertise to specific individuals
- Track customer emotion and sentiment over time
- Determine the customer segments that have the strongest feelings about your brand
- Track user behavior changes
- Discover key detractors and promoters of your brand
If you’re looking for more insights on your customers, you can’t go wrong with Sentiment Analysis. Companies can use it to adjust marketing strategies based on customer response. It can also help you measure the ROI of marketing campaigns and improve service to customers because you understand their emotions.
Wrapping It Up
Most major brands use social media listening to improve the overall experience of their customers. If you’re interested in learning more about this tool, we invite you to contact us to discuss the different Sentiment Analysis algorithms you can devise to achieve better customer service and an improved bottom line.