Understanding how sentiment analysis works
Sentiment analysis uses several technologies to distill all your customers’ words into a single, actionable item. The process of sentiment analysis follows these four steps:
1. Breaking down the text into components: sentences, phrases, tokens, and parts of speech.
2. Identifying each phrase and component.
3. Assigning a sentiment score to each phrase with plus or minus points.
4. Combining scores for a final sentiment analysis.
By remembering descriptive words and phrases to assign them a sentiment weight, you and your team can build a sentiment library. Through manual scoring, your team decides how strong or weak each word should be, and the polarity of the corresponding phrase score, noting if it is positive, negative, or neutral. Multilingual sentiment analysis engines also must maintain unique libraries for every language they support through consistent scoring, new phrases, and the removal of irrelevant terms.
Sentiment analysis can distill these approaches into three different categories:
A mix of statistics, NLP, and machine-learning algorithms to identify sentiments. The system is trained to associate inputs with corresponding outputs, that is, customer text with polarity. Machines are classified with the input data and can adapt over time once trained. This can be tested with additional data to provide better predictions.
The most straightforward sentiment analysis uses dictionaries or lexicons to explore words and phrases and determine their associated sentiments. This type of approach works well with direct and explicit opinions. While this system is fast and easy to use, it rarely considers how words are combined in a sequence. Teams need to add rules for comparative opinions as this approach can’t readily understand implicit opinions.
Combining both rule-based and automated systems means you can gain the accuracy and precision you need to truly understand your customers. This is the most powerful system as it contains the emotional information gathered from lexicons, which can be adapted over time.