How Sentiment Analysis Can Control Your Brand (and How to Get Started)

We’ve all heard of sentiment analysis, but what exactly is it and what can it do for your brand, your business, and how can you get started?

What is Sentiment Analysis?

Sentiment analysis refers to the analysis of content such as social media comments, customer feedback, employee feedback, and even facial expressions in images to infer sentiment orientation. These feelings can be as wide as simply saying that a particular content is a “adversary” or “promoter”, Or it can be as detailed as all of the above emotions within the content.

Fine-grained sentiment prediction in images

Why is sentiment analysis important in business?

While on the surface, sentiment analysis may seem like a fancy class project, it actually has many uses in business. Let’s look at some examples of sentiment analysis applied to business problems.

1. You can Aggregating customer sentiment from free-form feedback data And determine whether your customers are primarily promoters or detractors. After that, you can take corrective measures gradually Rebuild trust with adversaries and turn them into promoters.
2. You can Keep your online platform clean and free from bullying By exposing hateful and inappropriate comments.
3. you can define whose employees are demotivated or intend to leave based on recent feedback, peer review and manager feedback and provide a constructive way for employees to succeed in the company.

Overall, as you can see from these sentiment analysis examples, sentiment analysis is a versatile tool that can help you better understand employees and customers, keep platforms secure, provide customers with a better shopping and product selection experience, and learn from competing brands.

Most importantly, when you combine sentiment analysis With other AI-driven technologies like text summary, You can gain deeper, more powerful insights.

How are businesses using sentiment analysis? (real world examples)

Now that we know what sentiment analysis can help with, let’s see how three companies are using sentiment analysis for a specific business goal.

Gail

Great Wolf Lodge (GWL), a chain of resorts and indoor water parks, has expanded its extensive digital strategy by using artificial intelligence to classify customer comments based on sentiment. They developed what is called Grand Wolf Lodge Artificial Intelligence Lexicographer (GAIL).

GWL uses the Net Promoter Score (NPS) concept to measure the experience of individual users.

Instead of using the NPS score to determine customer satisfaction, GAIL determines whether customers are satisfied Network promoters, devastatings, or neutral parties Based on free text responses published in monthly customer surveys. This is analogous to predicting whether consumer sentiment is present Positive, negativeor neutral. GAIL essentially “reads” the comments and forms an opinion.

Through these efforts, the company hopes to better understand its visitors and improve the customer experience. For example, by analyzing reviewer comments, Great Wolf Lodge knew areas of their service that needed improvement.

It would take too much time for humans to manually analyze this unstructured data. However, GAIL can Analyze this data in seconds and determine whether the author is a pure promoter, detractor, or neutral party.

Meta

Meta has nearly 1.7 billion daily active users—naturally, content posted on the platform that violates its rules. Among these negative contents hate speech. Defining and detecting hate speech is one of the biggest political and technical challenges for Meta and similar platforms. Hate speech detection is a sentiment analysis problem that focuses on content with common negative implications.

Humans review AI-flagged posts in the same way as user-reported posts. In fact, the platform removed 9.6 million pieces of content flagged as hate speech in the first quarter of 2020 alone. While sentiment models alone may not be enough to control hate speech on a platform, the tool catches a large number of spam posts, significantly reducing the amount of manual work people do.

The extent of AI-based hate speech removal on Facebook. Source: Wired

Identifying hate speech content is a difficult problem. AI algorithms must understand the subtle meanings of text and the nuances of expressions, analyze the cultural context, and then determine whether it is offensive without wrongly penalizing innocuous content.

An example of hate speech. Source: arxiv.org

Ocean spray

When the morning juice market weakened, Ocean Spray, an agricultural cooperative of blueberry and grapefruit growers, looked for a new strategy to improve sales. Ocean Spray first needed to better understand consumer sentiment and behaviors around cranberry juice so they could innovate.

As a rule, such innovation takes place with the help of small focus groups of 10-15 people. However, Ocean Spray decided to use AI-driven analysis of thousands of online conversations, such as customer reviews and tweets about cranberry juice, to really Listen to the scale.

Additionally, instead of just classifying content like Meta, Ocean Spray leveraged topics and Summaries of Opinions To understand consumer sentiment around specific topics. Through this analysis, Ocean Spray understood how consumers used cranberry juice in real life, giving them ideas on how to better innovate and fill market gaps.

Research has revealed unexpected user behaviors. For example, they found that women enjoyed cranberry juice as a substitute for non-alcoholic beverages instead of cocktails. Such insights have helped them launch two new beverage lines, increase revenues and break out of an oversaturated market segment.

How to start sentiment analysis

As you’ve seen in this article, sentiment analysis has many nuances—you can detect sentiment in sentences, paragraphs of text, and even facial expressions in images. In addition, you have a variety of ways to use sentiment information, from using it to innovate a new product to improving the customer experience.

To begin sentiment analysis, you must first understand your business statement. Consider these questions:

• What do you want to know about your brand, customers or employees?
• How detailed should the information be?
• Do you need only sentimental information or text topics and summaries?
• Do you plan to integrate the solution into your dashboards or perform independent analysis?

Let’s take an example. Say you need to understand the general mood of your company Support conversations. You want to understand the current “tone” and “mood” of your customers. Additionally, you want to visualize this in your dashboards. In such a case, you will need employment Emotion classifier to make predictions appropriate conversations. You can then use these sentiments for downstream analysis in your dashboards.

Depending on your sentiment analysis problem, in some cases you will need to create your own classifiers. But for others, you can use off-the-shelf tools like Google’s Natural Language API or Perspective API.

Often, for multivariate analysis, you need to combine Off-the-shelf tools with custom pipelines and analysis It will help you answer all the questions to make the optimal decision. This is what one of my clients did. They combined insights from independent off-the-shelf text analytics tools such as netbase (very expensive by the way) with custom pipelines for full market research analysis.

There are endless possibilities for how you can use these sentiment analysis tools. But don’t forget to let the application guide the decisions you use.

Now about you. What applications of sentiment analysis come to mind after reading this article? What tools will you use for your analysis?

Keep learning from me:

• Join my AI Integrated Newsletter, which demystifies AI and teaches you how to successfully deploy AI to drive profitability and growth in your business.
• Read the business case for AI Learn the applications, strategies, and best practices to be successful with AI (select companies using the book: government agencies, automakers like Mercedes Benz, beverage manufacturers, and e-commerce companies like Flipkart).

Reading is recommended

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