Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique used to analyze a set of textual data and determine whether the sentiment behind it is positive, negative, or neutral.
This technique has become very useful in recent years, as it helps brands and companies to take textual data from the internet — such as customer reviews and social media comments — and automatically detect the sentiment of each written sentence. The goal is to be able to capture customer feedback and shape future business strategies around it. Thanks to sentiment analysis, companies can measure sentiment in relation to their brand or a specific product in real time, using social media or online reviews as a source.
The methodology of sentiment analysis tools
The main technologies used to perform sentiment analysis are natural language processing (NLP) and machine learning algorithms. These tools train themselves to process textual data, detect the emotions behind the word and the sentences, and categorize them automatically without the need for human input.
Depending on the scale of the operations, businesses may use different tools and algorithms to perform sentiment analysis. These algorithms can be typically be split into three categories:
- Rule-based: The user is required to manually set the rules the tool will apply to perform the sentiment analysis.
- Automatic: The tool uses machine learning algorithms to automatically perform the analysis without the need for manual input on the user’s end.
- Hybrid: The tool combines automatic and rule-based methodologies to perform the analysis.
Common types of sentiment-analysis data
There are different types of data that sentiment analysis tools target. The tools performing the analysis usually focus on polarity (the experience described by the consumer is positive, negative, or neutral) and detecting emotions and feelings (the consumer feels upset, disappointed, frustrated, happy, etc.).
The categories can be set up to be more or less granular according to the company’s needs. For example, polarity can be defined in more precise terms (from very positive to very negative). Five-star ratings can also be used to detect polarity.
More granular tools for sentiment analysis seek a deeper level of detail, understanding how the customer feels about different aspects related to the product and company. For example, in an online product review, a customer might praise the product’s functionality, but criticize its design; or they might criticize the packaging, but praise the company’s customer service.