Sentiment Analysis is the process of determining whether a piece of writing is negative, neutral or positive. A sentiment analysis system for text analysis combines Natural Language Processing (NLP) and machine learning techniques to assign weighted sentiment scores to the entities, topics, themes and categories within a sentence or phrase.
By analyzing large volumes of reviews, social media comments or articles, companies can gather information on how people feel about them, their products, or their services.
This information can be used to improve the products or services, but also to find new opportunities or improve the overall image of the company. Within Pon Datalab, this technology is used in for instance the Media Monitor. Here we analyze news items, articles and social media text and determine how people talk about your brand.
Sentiment analysis can be used for large volumes of data. Where a human interpreter would take days to analyze thousands of tweets, a computer can this within minutes or even seconds. On top of this, the analysis can be done in real-time. This allows a company to constantly monitor what is happening online, and react on possible good or bad situations. Another advantage of sentiment analysis is the consistency it has with analyzing text data. The criteria on which the computer scores text will always be the same, and will not be influenced by personal experiences, thoughts, or beliefs. This helps reduce errors and improve data consistency.
Target individuals to improve their service
By capturing customers who feel strongly negative towards your product or service, customer service can deal with their issues specifically. Imagine the fury of a customer who leaves a comment that is very negative. Whether it is through personal contact or through prioritizing their tickets, action can help defuse the situation.
Track customer sentiment over time
Tracking customer sentiment attached to specific aspects of the business is very effective. Analysis can explain why your image has changed; or if it has remained the same, what may have changed in the aspects.
Determine if particular customer segments feel more strongly about your company
When paired with demographic and other quantitative data it is possible to segment the customer base and look at their sentiment in isolation. For example, do customers who spend less feel more negatively (and therefore it is a barrier to them spending more) or are the return policy issues from customers in Miami and not those in New York?
Track how a change in product or service affects how customers feel
As the business changes so does the customer sentiment. Publishing a marketing campaign or press release, changing your product’s interface or price structure can have an effect. Tracking customer sentiment can measure this. A change in score can indicate if a change has resonated with customers emotionally and was successful. Tracking the sentiment will help you fix any blunders quickly.
Determine your key promoters and detractors
Customers may be commenting on many aspects of your business, but which areas are affecting your business image? A little bit of data science will help you answer this question. By correlating aspects with promoters and detractors it is possible to show what influences how people feel about you and your company.
Socialbakers: “Social Media Sentiment Analysis”,
Monkeylearn: “Sentiment Analysis”,
McKinsey: “The ‘moment of truth’ in customer service”,
Towardsdatascience: “Five Practical Use Cases Of Customer Sentiment Analysis For NPS”