What do you mean by Aspect-Based Sentiment Analysis?
Aspect-based sentiment analysis is an ML technique that utilizes machine learning to extract detailed and actionable insights from customer feedback data. By breaking down the data into smaller categories, it uncovers hidden sentiments related to specific aspects of a brand.
This technique examines data from diverse sources such as social media comments, videos, reviews, online publications, and surveys. It aims to identify the features and aspects of a business that require improvement in order to enhance revenue.
Aspect-based sentiment analysis is one of the three levels of sentiment analysis, alongside Document-based and Topic-based sentiment analysis. These algorithms work in conjunction with named entity recognition (NER), natural language processing (NLP), and other AI techniques to assess sentiment.
Document-based sentiment analysis analyzes simple sentences and provides a brief overview of expressed emotions. Topic-based sentiment analysis, on the other hand, examines sentiment in larger datasets by identifying words and phrases and grouping them into specific topics like "food" or "customer service," calculating sentiments for each topic.
Aspect-based sentiment analysis is the most advanced of the three levels. It extracts aspects from the data, measures their sentiment, and assigns them to previously identified topics. For instance, it can identify aspects such as "quick service," "polite staff," and "cleanliness," assess their sentiment, and aggregate them under the topic of "customer service." This approach provides both topic-based and aspect-based sentiment insights.
By utilizing machine learning models specifically tailored to industry-specific aspects, aspect-based sentiment analysis delivers highly accurate insights. This accuracy stems from the model's ability to focus on industry-specific details within the data. It is crucial because aspects vary across industries. For example, in the banking industry, aspects like "teller" or "savings account" have no relevance to aspects such as "food" or "drinks" in the restaurant industry. This built-in capability allows brands to automatically receive customer sentiment insights pertaining to various aspects of their business without the need for manual tagging or labeling of industry-specific topics and keywords.
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