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. Sentiment analysis at aspect level first determines 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.
The first step for aspect based granularity in sentiment analysis is model generation. Using machine learning and a neural network designed for natural language processing, Repustate is able to cluster words and phrases found in text documents into semantically similar clusters, or aspects and then derive a sentiment score for each aspect by using the sentiment analysis API.
This means phrases like "very expensive", "good value for money", "too costly" are all grouped together at a semantic level. Repustate then applies a user-friendly label, like "Price", to each automatically discovered cluster. For example, when analyzing a restaurant these clusters might be "Price", "Quality", "Service", and "Menu variety".
Once the machine learned model has been trained, Repustate can now classify any new text data and identify each and every aspect present in the data. For each aspect found, a sentiment score is determined. This means you can get multiple sentiment scores for each text document, varying in sentiment from positive to negative. And this is exactly what you want as in real-life situations, people often express varying sentiments about the same subject matter.
Aspect based sentiment analysis is by far the quickest way to surface insights and gain a more nuanced understanding of your customer's opinions.
Aspect based granularity in sentiment extraction excels wherever user generated content and product reviews are required to be analyzed.
Analyzing product aspects in reviews on large ecommerce sites or analyzing traveller reviews left on large travel aggregator sites are common applications of aspect level 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 sentiment in social posts, product reviews or customer surveys, aspect-based sentiment analysis enables more granular insights about customers' mindsets, improving business performance.
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 level 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.