At its core, sentiment analysis is judging the opinion of a piece of writing. It means taking a series of words and judging whether it falls under positive, negative or neutral.
Sentiment analysis is useful because it helps gauge public opinion of an event or a product. Customers often rant on spaces like Twitter, leave reviews on Amazon, or express both positive and negative emotions on social media. Sentiment analysis helps wade through that data, and give and figure out what people really think.
How Does Sentiment Analysis Work?
Imagine that you are in charge of a small coffee chain, and you want to know how your coffee is being viewed by customers. So you start going going through tweets directed at your company.
“I love this coffee”
“I hate the taste of coffee”
This is simple enough. Any basic sentiment analysis tool will tell you the first tweet is positive, and the second tweet is negative. But human tweets (or rather, expression) are often much more complicated than that. They convey a wide range of emotions, and often require context to fully understand. Look at the following tweets:
“I love the Espresso, but can’t stand the Macchiato at this place”
“Wish this brand would update its packaging!”
“Can’t believe the server handed me a cold coffee instead of hot – JUST GREAT!”
The first isn’t neutral, because of both ‘love’ and ‘can’t stand’, but instead conveys both a positive and a negative emotion. The second tweet describes a partially negative emotion, but isn’t a downright dismissal of the product, just an aspiration. As for the third tweet, while ‘Just Great!’ is a positive statement, the tone is heavily sarcastic and therefore negative.
A good sentiment analysis tool should be able to accurately identify each tweet. But how do you get there? You have to provide thousands of examples of pre-labelled data to train your system. This manual annotation of sentences forms the basis of machine learning. The more data and the better labeled it is, the more accurate the tool.
One of the challenges of manual annotation is in making sure that humans also accurately identify sentences. As noted above, it isn’t enough to bucket things under positive, negative or neutral. Grey areas that human annotators should pick up include sarcasm, irony, and mixed feelings.
What are some useful sentiment analysis tools?
Many basic sentiment analysis tools are for use within existing platforms, including Google Insights, Google Alerts, and Facebook Insights. Others include Brandwatch, Hootsuite Insights, Meltwater, and Opentext.
Why Choose Lionbridge AI’s Sentiment Analysis Services?
Now, instead of a small coffee chain, you are in charge of a large coffee MNC that sells in different international markets. You not only need to know how English twitter feeds feels subjectively about your coffee, but also Mandarin and Arabic twitter feeds.
You already have a sentiment analysis tool – perhaps one of the above – to filter through the tweets. Let’s say that this tool gives you about an 90% accuracy rate in English, but about a 60% accuracy rate in Arabic. You want to bring both accuracy rates up to 100%.
That’s where Lionbridge AI comes in. With over 500,000 vetted language experts each in 300 languages, we can provide the most accurate sentiment annotation in the market. With 20 years of experience in languages and translation, we help give you data for sentiment analysis.
If you’re looking for a deeper guide on what sentiment analysis is, check out our related resources below.