Businesses collect feedback from customers as well as internal employees to learn how to improve their products, services, and internal operations. When it comes to analyzing this feedback, being able to parse raw text reviews and categorize them by sentiment quickly is key to gaining quality insights efficiently.
Sentiment analysis helps you gauge the reputation of your brand and make smarter business decisions that match up with what your clients and staff want. Doing it right, however, requires a bit of work and integrations with all the channels where the feedback data resides.
What Is Sentiment Analysis?
Through natural language processing (NLP), computers can automatically determine whether a text review is:
- Positive, neutral, or negative
- Happy, sad, surprised, angry, disgusted, etc.
- Low importance, moderate importance, urgent
- Uninterested, mildly interested, very interested
This metric can be incredibly useful when soliciting feedback from customers after a sale or employees about company policies and the work experience.
Why Does It Matter?
The applications of sentiment analysis are numerous enough that almost every type of company from any industry can get use out of it. Some of the benefits include:
- Fast analytics: Because much of the process is automated, you can get a clear picture of consumer or staff sentiment quickly through sentiment analysis. Should an issue arise and customers get riled up, you can form your response promptly.
- Consistent results: Imagine you tried to sort reviews by sentiment manually. On top of the inefficiency, the choice of what sentiment to apply to each review varies depending on the person. By using automation, you can ensure consistent results every time.
- Scalability: You are likely to run into thousands upon thousands of social media posts, reviews, and survey responses. Like most automated processes, sentiment analysis is thankfully scalable.
Best of all, you don’t necessarily need a survey where users input a quantitative response such as a star rating. Thanks to the aforementioned NLP technology, automated sentiment analysis is capable of extracting these feelings directly from raw text data, such as social media posts and chat transcriptions.
Common Sentiment Analysis Use Cases
Whether you work in hospitality, manufacturing, or finance, sentiment analysis has found applications almost everywhere. Some specific use cases include:
- Customer service: Quality customer service keeps clients feeling valued and coming back to your brand. Analyzing sentiment after a service call not only helps you gain new customers but also keep the ones you have.
- Market research: Sentiment analysis can go beyond your own company. By analyzing the competition, you can take advantage of moments when a competitor’s product line or service falls flat.
- Social media analysis: Since most customers head to Twitter or Facebook to talk about their experiences, analyzing social media is the perfect way to gauge your brand’s image among the public.
- Staff surveys: Not only can sentiment analysis help your customers, but it can also help you quickly understand how your employees think about working with you. Making improvements to internal operations and policies for the better boosts staff morale and productivity.
It’s clear that sentiment analysis has widespread, almost ubiquitous appeal. All businesses rely on their clients and employees to run, so understanding the feelings among those groups is key to staying competitive.
How Do You Approach Sentiment Analysis?
Because sentiment analysis algorithms have to work with unstructured data, there’s no guarantee it can cover every potential circumstance when it comes to varied responses. For instance, the use of the following can make it difficult for a machine algorithm to parse intent correctly:
Not all natural language processing algorithms have the capabilities to get sentiment right every time, so how do you implement sentiment analysis properly?
Analysis Through Pre-Set Rules
You can manually craft basic rules to organize reviews by sentiment. Basic natural language processing techniques start by looking at word choice and incidence. Terms like “worst,” “best,” “good,” “awful,” “ugly,” and “beautiful” can point to obvious sentiment in most cases.
This approach has limitations for reasons we’ve mentioned before, such as the use of irony. While pre-set rules are the easy way in, automation through machine learning has started to pick up steam in the industry.
Analysis Through Automation
A business trains a machine learning algorithm by feeding it inputs (in this case, reviews, social media posts, and survey results) and teaches it the correct sentiment for each. Over time, the algorithm picks up on specific features of the input that indicate the resulting sentiment.
Machine learning is a complicated technology that we cannot fully explain in this article, but the potential advantages are too great for companies to ignore. In fact, it’s not uncommon to see sentiment analysis applications using both pre-set rules and machine learning together.