Aspect-based sentiment analysis

Aspect-based sentiment analysis is one of the three levels of sentiment analysis–the others being Document-based and Topic-based. These algorithms work together with named entity recognition (NER), natural language processing (NLP) and other AI techniques to measure sentiment.

Aspect-based sentiment analysis is a machine learning (ML) technique. It provides granular, actionable insights from customer feedback data by breaking it down into smaller categories to uncover and extract hidden brand sentiments.

The technique analyzes data from various sources—social media comments and videos, reviews, online publications, and surveys—and can help identify which features and aspects of a business need improvement to boost revenue.

Document-based sentiment analysis analyzes a whole piece of text and provides a single categorization of the sentiment expressed. at the emotions expressed. Topic-based sentiment analysis breaks pieces of text into words and phrases, clusters them in specific topics such as ‘food’ or ‘customer service’ and calculates sentiments for each of them.

Aspect-based sentiment analysis is the most advanced of the three. It mines aspects from data to measure their sentiment and attributes them to the topics that have been previously identified. For example, it will identify aspects such as ‘quick service’, ‘polite staff’ and ‘cleanliness’, measure their sentiment and collate them under the topic “customer service”. Thus giving you topic-based sentiments plus aspect-based ones.

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A machine learning model built on industry aspects provides higher accuracy insights because they are drawn from specifics in the data. This is key information because aspects in every industry differ. For example, aspects such as ‘teller’ or ‘savings account’ in the banking industry have no relation to aspects such as ‘food’ or ‘drinks’ in restaurants. With this in-built capability, brands can automatically receive customer sentiment insights about multiple aspects of their business without having to manually build tags or labels for topics and keywords relevant to their industry.

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