Sentiment analysis has taken the customer experience world by storm. It's one of the quickest ways to know how your customers feel outside of directly asking them, and can sometimes uncover hidden issues that they wouldn't feel comfortable expressing directly to your face or in an NPS survey. There are so many tools, both free and paid, that make it easy for companies to understand the emotions behind what their customers are saying.
In this post, we'll get into the details of what sentiment analysis is, how companies use it, and which tools you can implement to make it easier.
What is sentiment analysis?
Sentiment analysis, sometimes known as "opinion mining," is a strategy that uses technologies to interpret data as a way to understand emotions or feelings around a product, topic, or idea. Often it is done on text, such as email messages, social media responses, survey responses, or text messages. There are a few different types of sentiment analysis:
- Rule-based sentiment analysis: analysis performed using a set of manually-created rules
- Automatic sentiment analysis: analysis that depends on artificial intelligence
- Hybrid sentiment analysis: analysis that combines an automatic and rule-based approach
How does sentiment analysis work? Typically companies look to outsource sentiment analysis to a third-party system, like the ones we will be listing in this post. Then, they either work to create their own rules for analysis or provide historical text to guide their AI through a training period before launch.
What are the limitations of sentiment analysis?
As with all things, even though sentiment analysis is helpful for many cases, some are a bit limited. First, AI and machine learning in general struggle to understand the nuances of human speech. So, things like sarcasm, irony, jokes, hyperbole, and colloquialisms can confuse it.
For instance, a phrase including the word "disappointed" might trigger as negative for an AI without considering the words around it. But, in some cases, “disappointed” can be used in a positive context, like "I'm not disappointed." If your users write in short sentences or you're scraping Twitter regularly, phrases like this may confuse the AI without enough context to pull reliable analysis.
What are the best sentiment analysis tools?
Now that you know what it is and how to do it, here are eight of the best sentiment analysis tools for all price ranges and needs.
Talkwalker is a free product that offers many of the features that companies are looking for with sentiment analysis. This product claims to understand and identify sarcasm or some of the other common trip-ups mentioned above. Beyond just monitoring your customer's sentiment, Talkwalker pulls in industry data and trends to help your team benchmark against other companies' performance.
Mention is specifically designed to monitor social media and check for brand mentions on social networks, in the news, on websites, or in search results. It allows you to compare your sentiment ratings with those of your competitors to understand where you might be losing out, too. Just as with other sentiment analysis tools, Mention sorts all feedback into positive, neutral, and negative buckets so that you can better understand how your brand perception changes as your product and strategy do.
Kapiche is unique in that it allows you to gain a 360-view of the customer through analyzing a wide range of sources. Kapiche uses a combination of structured and unstructured data from surveys, call transcripts, eNPS, and email to give you the most holistic understanding of sentiment for all the groups you are curious about. Sentiment analysis forms one very small part of the Kapiche platform - to see what else is on offer, request your free personalised demo here.
Lexalytics is a business intelligence (BI) product that is also based on text analysis. Like other sentiment analysis products, Lexalytics scrapes social media, reviews, and any other text artifacts that it might find about your product on the internet. Beyond that, as you can see in the screenshot above, the tool also does intention detection and theme extraction, which can help provide a much larger picture of your holistic customer experience.
Clarabridge, as a product, does quite a bit more than sentiment analysis. That said, with it’s sentiment analysis piece, it uses a rule-based approach, as opposed to hybrid or strictly AI. While consumers would likely get more benefit if they were using the whole product holistically, Clarabridge's analysis is extremely complex and allows companies to dive deeper into their consumer's data. For instance, you can see the grammar, context, industry of the person responsible for the comment, and much more.
MeaningCloud is notable because of its product’s accessible API. This access is handy if you already have a lot of raw data you are looking to analyze and understand more deeply. It also enables a straightforward method to access multilingual analysis, which can sometimes add additional complexity to a product. MeaningCloud uses a hybrid approach of AI and self-defined rules, which make it pretty flexible for many different sizes and types of companies.
Rosette is another API-driven, multilingual sentiment analysis tool. It is a bit more basic than some of the other offerings, making it attractive to companies just starting to do sentiment analysis. Rosette's sentiment analysis tool primarily works with social media data, rather than pulling information from a number of text sources.
Social Searcher is a free product specifically designed for social media monitoring. It is not multilingual and doesn't monitor anything beyond social media, but it is suitable for companies looking to get started for free. It currently processes 11 different social media sources, like Facebook, Twitter, Youtube, and Vimeo.