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.
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.