Since 2010 automated sentiment analysis has been a source of debate around the Net with questions like: What is this methodology? How does sentiment analysis work? In what context is it useful for a business? But the truth is that what is useful for a company may not be for others. In this post we will answer some of these questions:
- What is sentiment analysis
- How does it work
- Tips to apply it to your business strategy
Sentiment analysis or opinion mining can be defined as a solution that using different technologies _such as natural language processing, text analysis, and computational linguistics_ allows you to determine if pieces of writing are positive, negative or neutral. However, there is a key that differentiates the performance of sentiment analysis: deep linguistic analysis approach. Grammar based it allows opinion analysis not only at the sentence level but also at the phrase level within the sentence. Here you can see an example for clarification:
How does it work? Below you can see an image from the final result. But to achieve that the tool use the following processes:
- Parser: every sentiment analysis tool uses some kind of parser. Our one is needed to match the topic with the sentiment text.
- Dictionaries with relevant expressions so it can extract the sentiment from the text and also provides you a score for that sentiment: for example good will be +1, very good +2 and excellent +3 on the contrary awful will be -2.
- Secret Sauce: this is up to the different providers. Here at Bitext it allows us to analyze complicated sentences as the ones written in comparative or conditional. We are able to detect the difference between "The meal was bad" and "The meal was not bad at all".
Every tool you decide to apply to your processes will help you, so a priori the answer to will sentiment analysis help my business to grow is YES. However, that answer is not enough, so let's put some examples to show the utility:
- Let's imagine your company has conducted a survey to analyze customer's feedback after the service to see areas of improvement. If you were interviewing around 50 clients it will be quicker to just read them and extract conclusions. But 50 is not a significant sample so let's expand it to 200, in this case, you will need more than one person to analyze all of them and another problem shows up: how can two different people classify opinions in the same way? Sentiment analysis, in this case, will provide you unbiased results saving time.
- As we mention last week, a large part of your clients are using social media, particularly Twitter to talk and express their opinions. In high competitive markets, it's important to be above your competitors providing your clients what they are looking for, and part of this is support and responses. Like in the example above analyzing dozens of tweets every day, it's very time-consuming, also it's a difficult process. Someone gives responses to users but he may not have all the answers so asking is required and sometimes it will end up questioning the same department more than once the same day. In this case, sentiment analysis allows you to collect all your daily tweets analyze them, group them by categories and send the information all in once and also review after customer's feedback to improve some of your business areas.
the best way to see how text analysis can be useful for you is trying it with your own data, click down below to check out our API!