Product reviews, social media posts, and discussion forums are a major source of information for businesses for analyzing customer feedback. Natural Language Processing (NLP), and specifically sentiment analysis, provides an automated way to categorize this feedback into positive, neutral, and negative categories. Using this information, businesses can identify trends in customer sentiment, find what drives customer satisfaction, and react to negative feedback.
With the release of the new Opinion Mining feature in Text Analytics, we provide a new tool for analyzing user-generated content. Opinion Mining is an implementation of aspect-based sentiment analysis which goes beyond identifying the sentiment to provide more insights from text data. Using this new tool, businesses can extract customers’ sentiment and opinions around specific aspects or attributes of a product or service.
For example, in a review comment regarding a hotel stay such as “I liked the pool, but the staff was unfriendly”, Opinion Mining would identify the following:
|
Aspect |
Opinion |
Sentiment |
|
Pool |
Liked |
Positive |
|
Staff |
Unfriendly |
Negative |
Aggregating this data can highlight trends and provide deeper understanding of customer sentiment, breaking down the data into specific areas the business can dive into.
Image showing multiple aspects and opinions from hotel reviews
Opinion Mining can add greater depth to understanding of customer sentiment and provide a more granular view of the data in voice of customer, customer feedback analytics, and call center analytics solutions. Customer feedback that mentions a competitor’s product features can be used to perform competitive analysis. And combined with customer purchase history and profile data from CRM systems, it can provide a powerful way to analyze and guide product design and launch, brand strategy, and advertising and marketing campaigns.
Get started
For more information on how to use sign up and use Opinion Mining, see the documentation.