Sentiment analysis relates to the methods, techniques and processes used to retrieve information about a consumer’s perception of a product, service or brand.
Tools for sentiment analysis use machine learning algorithms to gather data about consumer attitude and evaluate the emotional state of a consumer. In marketing, most companies turn to sentiment analysis through customer service and social media monitoring.
The digital world allows people to form opinions quickly and share their thoughts on a host of channels. Companies who know how consumers feel about their products and services are better equipped to improve sentiment.
How brands use sentiment analysis
Sentiment is an emotion, feeling or opinion.
On social media, a person’s sentiment is evident in their tone or the way they talk about a brand.
Sentiment analysis is useful in social media monitoring, as it offers an overview into public opinion around your brand. Social media tools like Sprout Social simplifies the process of analyzing sentiment, by tracking positive or negative context around brand mentions.
With a tool for sentiment analysis, companies extract helpful insights from social data. Many social listening tools allow businesses to filter mentions by sentiment to focus on the issues that need to be dealt with as quickly as possible. For instance, sentiment analysis is essential when businesses need to:
- Watch for PR and social crises
- Find and assist unhappy customers
- Respond to negative feedback
Issues with sentiment analysis
Though sentiment analysis is an exciting concept in the digital age, it’s not without flaws. Sentiment analysis works by using machine learning techniques. Artificial intelligence bots are given vast amounts of data to compare new data sets against in the future. This means that every time your bot gets a new mention to analyze for sentiment, it goes back and looks at the message against the information it already has.
Unfortunately, though technicians have discovered how to give machines a basic understanding of human language, these devices often struggle with concepts like slang, sarcasm, and skepticism.
For instance, someone on Facebook saying, “My product arrived broken! Brilliant!” is clearly unhappy. However, it’s possible for the bot to categorize the word “brilliant” as positive, reducing the accuracy of your sentiment analysis strategy. Human emotion is complicated, and experts are still working on making sentiment analysis more effective.
The benefits of sentiment analysis
Though there are bugs to work out before sentiment analysis is perfect, the potential benefits are astronomical. Companies collect data on sentiment trends and use it to understand how their brand reputation is changing over time. Additionally, sentiment analysis also offers useful insights into customer emotion to make proactive decisions about your company’s future.
Used correctly, sentiment analysis:
- Provides insights into your audience: Find out how your customers feel about their brand, products, and services.
- Measures the outcomes of marketing campaigns: Review the success of marketing programs based on changes in customer sentiment.
- Supports customer service: Listening for changes in customer reactions may help brands to offer quicker resolutions to problems over social media.
- Supplements positive PR practices: Tracking sentiment helps brands to keep track of any negative mentions or comments they need to address quickly. The faster you know about a problem, the quicker you can stop the issue from spreading.