Unlock the meaning in your data.

Our AI-driven aspect based sentiment API identifies all of the semantic concepts in your text and calculates the sentiment for each, leading to more granular insights faster.

What is aspect based sentiment?

Aspect based sentiment analysis goes one step further than typical sentiment analysis by automatically assigning sentiment to predefined categories.

It involves breaking down text into smaller chunks, allowing more granular and accurate insights from data. With aspect based sentiment analysis, it can be distinguished which features of a product or service offering are liked and which ones can be improved.

In typical reviews, consumers often touch on many aspects of a product or service. Complaints or praise for price, quality or ease of use can all be mentioned in one comment. Aspect based sentiment analysis determines first which categories are being mentioned and then calculates the sentiment for each of those categories. When compiled in aggregate across a large number of reviews, the strengths and weaknesses of a business' product or services surface quickly and actionable insights become obvious instantly.

How does aspect based sentiment work?

Aspect based sentiment analysis works by associating sentiment with each semantically similar cluster, or aspect, found in text. Using machine learning, Repustate takes text and automatically determines how to segment data into semantically similar clusters of words and phrases. For example, when analyzing a restaurant these clusters might be price, quality of food, service and menu variety. Once the machine learned model has been trained, Repustate classifies the text and identifies each and every aspect present in the data. For each aspect found, a sentiment score is determined meaning one input text can have multiple sentiment scores associated with it.


Aspect based sentiment excels wherever user generated content and product reviews are required to be analyzed.

Analyzing product reviews on large ecommerce sites or analyzing traveller reviews left on large travel aggregator sites are common applications of aspect-based sentiment analysis.

Any company that relies on customer feedback in order to improve their product or service should be utilizing aspect-based sentiment analysis in order to see which areas are successful and which need more care and attention. Whether analyzing social media interactions, product reviews or customer surveys, aspect-based sentiment analysis enables more granular insights about customers’ mindsets, improving business performance.

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Additionally, regardless of industry, classifying and analyzing employee feedback is greatly improved by leveraging Repustate’s aspect-based sentiment analysis. From employee dissatisfaction to improvement suggestions, it is simple to discover which areas of a company need attention and why. Aspect based sentiment analysis delivers insight quickly and accurately and speeds up the feedback loop.

Aspect based sentiment can be used in any application where the primary subject matter is written about in a variety of different ways, using a variety of terms or phrases but at the same time, can be semantically clustered and grouped into several distinct categories.


The industries that benefit most from Repustate’s aspect-based sentiment analysis are those that deal with a large quantity of user generated content and reviews. Companies that are customer-centric and rely on the voice of customer benefit greatly from aspect-based sentiment analysis.