What is Sentiment Analysis?
Sentiment analysis, also known as opinion mining, is a form of data analytics that involves mining text in order to determine if the data is neutral, positive, or negative. This form of data analysis involves natural language processing techniques. It is often applied to textual data that has been posted online so that a business can evaluate the customer feedback on a given brand, as well as to better assess the needs of its customers.
Sentiment analysis plays an integral role in a business because it helps to transform customers’ emotions and attitudes about a product or topic into actionable insights. Because sentiment extraction is an automated process, it drastically reduces the time and resources that would be required for a human to perform the same tasks.
This article will explore some of the ways sentiment analysis is currently being used by businesses to gain valuable information from the sentiments of their customers.
How is Sentiment Analysis Used in the Real World?
Sentiment analysis has many applications for businesses in a variety of sectors. The following are some of the most common real-world uses of sentiment analysis:
- Market research. Because it works with a large set of data, sentiment analysis lends itself well to most kinds of market research. It has applications for studying entire markets, as well as segments, specific products, or features. With the help of sentiment analysis, it’s possible for marketers to execute tailored market investigations that will ultimately be used to inform the decision-making process.
- Brand reputation. Many people who purchase products turn to the internet to share their opinions about how pleased or dissatisfied they are with their selection. They share their experiences and thoughts in many forms, such as product reviews, social platforms, blogs, and discussion forums. By gathering and processing these comments, it’s possible to gain important business information that helps to maintain the reputation of a brand.
- Improving services. Companies such as Uber rely on sentiment analysis when they monitor social media to gather information pertaining to whether users are pleased with the newest version of their app.
- Movie reviews. Review websites such as Rotten Tomatoes employ sentiment analysis to analyze movie reviews. Rotten Tomatoes performs sentiment analysis on huge stores of subjective opinion data. By using five values: negative, somewhat negative, neutral, somewhat positive, and positive, labels can be created that can classify all of the movie review phrases currently in the database. However, challenges occur when sentiments such as ambiguous language, brevity, sentence negation, or sarcasm are present in a review, all of which pose challenges for natural language processing tools that are designed to focus on the words rather than the intentions behind them.
- Politics. Political Scientists rely on sentiment analysis to determine how announcements are received by the public. In 2012, the Obama administration applied sentiment analysis to evaluate policy announcements. In addition, this form of analytics can be used to study the number of negative mentions about candidates in various news and media sources.
- Customer support. Over a quarter of those who have one bad customer service experience decide to drop the product or brand. With the growing popularity of social media, those who vocalize their negative customer service experience online have the potential to adversely affect a business in a potentially large-scale manner. Yet effectively managing customer support can be a challenging process. Given the large number of requests that must be processed and evaluated, as well as the diverging topics and various levels of urgency of various requests, applying segment analysis can be very helpful for streamlining the process. Using natural language understanding tools, a business can automatically process a large number of phone calls, emails, and online chats, and classify them into categories based on common traits.
- Employee satisfaction. Businesses perform best when their employees are happy. Applying sentiment analysis can help an organization learn how engaged its employees are, how productive they have been, and ultimately how best to reduce turnover and ensure that workers are satisfied. By applying artificial intelligence to Slack messages, Glassdoor reviews, and employee surveys, it’s possible to subjectively evaluate employee feedback and to discover the most common concerns and points of discussion.
- Preventing brand crises. Tools like Brand24 are useful for monitoring social media in real-time. They gather all of the mentions of predefined keywords from sources such as news sites, websites, and discussion forums. This ensures that PR specialists are alerted about negative comments immediately after they appear so that the company can put measures in place to respond immediately before it becomes a large problem.
- Finance. The process of making solid investments in the business world can be a tricky one. The stock market can be volatile and often fluctuates drastically in a short amount of time. However, some of the variables that affect the stock market can be considered before investing. For example, when deciding between investing in two automobile companies, the sentiments received from the company about their latest model can be evaluated. This can indicate which company is performing better in the current market and the decision can be made accordingly.
- Improving Products. Those who wish to evaluate how well a given product is working can use sentiment analysis to hone in on specific product features. When a feature or several features of a product are segmented in the analytics process, it’s possible to tell if a feature is as popular as it is thought to be, or if customers are having a different reaction to it altogether. In addition, marketing campaigns can be designed to focus on specific groups who have expressed interest in a feature. It can be particularly helpful to know if a large number of users feel the same way about specific product features.
As technologies involving artificial intelligence, machine learning, and deep learning are used more and more in the data analytics process, sentiment analysis is expected to become more mainstream, and will likely have further applications for small companies, as well as for the general public.
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