Features of Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is a data mining technique that automatically assigns sentiment to predefined aspects or features of a business. In this blog, we learn about the distinct features of aspect-based sentiment analysis that make it superior to other types of techniques like document-based and topic-based sentiment analysis.
What Is Aspect-based Sentiment Analysis?
Aspect-based sentiment analysis(ABSA) is a machine learning task that identifies and assigns sentiment to aspects, features, and topics that it has categorized in the body of data. Insights gained from ABSA are more detailed and in-depth than other techniques that give a more general overview of sentiment. Where document-based sentiment analysis determines sentiment from a piece of text giving you a straightforward answer based on certain keywords, topic-based sentiment analysis tells you the sentiment for a certain topic in the data. When it comes to ABSA, advanced features of aspect-based sentiment analysis help it extract granular information from the data thus giving you a holistic view of the customer’s feelings towards various aspects of your product/service and brand.
For example, in a sentence like “The appetizers were okay, the drinks were flat, and the ambiance was very bad.” Here document-based sentiment analysis will read the text as negative as it takes the overall sentiment. Topic-based will tell you that the topic “food” has neutral sentiment, while “ambiance” is positive. Aspect-based sentiment analysis, on the other hand, breaks the text down and searches for not only topics but also aspects like food, drink, atmosphere, etc. It will tell you, that with respect to the topic “food”, customer sentiment was neutral, leaning towards the negative on drinks (it does this through semantics and contextual processing), while with regards to the atmosphere in the restaurant itself, it will tell you that customers were negatively inclined.
In this way, it doesn’t just tell you what the consumer sentiment is but gives you details that you can use to improve customer experience.
What Are The Advantages Of Aspect-Based Sentiment Analysis?
From the example above you can clearly see how aspect-based sentiment analysis is far more advanced in emotion mining than other methodologies. There are certain features that give it this complex capability for granular sentiment analysis. Let’s look at them closely.
1. Semantic clustering
One of the most important features of aspect-based sentiment analysis is semantic clustering. When data is processed through a sentiment analysis API, it undergoes text analytics. Video content analysis ensures that even if the source is in a video or audio format, it is processed accurately by converting it into text. Now that all the data is collected, natural language processing (NLP) algorithms and named entity recognition (NER) help segregate the text into categories, topics, aspects, and entities.
Semantic clustering ensures that there is no redundancy of data by putting all words with semantic similarity together. In an advanced platform like Repustate IQ, semantic clustering occurs with the same accuracy if the data is in a non-English language as with English. This is because the platform reads each language natively, without using translations. For this, it has dedicated speech taggers for each language, with an exhaustive list of words that includes not only general words but also geographical locations, objects, famous people, currency nomenclature, etc.
In this way, semantics and semantic clustering as features of aspect-based sentiment analysis help it to derive accurate customer or employee insights without false negatives or positives. This efficiency improves with each iteration because the AI algorithms learn with each cycle of data processed thus becoming smarter each time and adding more to their vocabulary for semantic clustering.
2. Feature analysis
Aspect-based sentiment analysis segregates big data into blocks that it further scrutinizes by extracting features of the subject from it even if it’s not explicitly mentioned. For example, let’s look at this real-life review of the Smashbox Studio Skin Full Coverage Foundation.
In the review above, aspect-based sentiment analysis will identify which Smashbox product (foundation) the customer was talking about, and which line it was from (Studio Skin full coverage). The feature analysis capability of ABSA would not only tell you what aspects of the product were good or bad but also identify those features even though they are not mentioned in the text. In this case, because the customer says “doesn’t cover my acne or other skin marks”, ABSA recognizes that she is talking about the “coverage’ of the product. Similarly, when she says “my skin looks really flaky with this product”, the aspect-based sentiment analysis recognizes that she is talking about the “texture” of the product.
And even though she does not use any negative words like “bad”, or “unhappy”, and rather ends her review with “but it worked well”, the machine learning algorithm still would know that the customer was disappointed in the product. It does all this while giving the company insights on how it can make the product better by working on the two features it extracted from the text. (coverage and texture).
The same process happens with video data, whether it is to give you the analysis of a review video on YouTube, or TikTok insights when it analyzes hundreds of comments about the product.
3. Aspect similarity co-occurrence
Another one of the key features of aspect-based sentiment analysis is aspect similarity co-occurrence. This feature helps the algorithm identify aspects that occur together quite frequently and this frequency tells it how significant this word group is, in the context of the industry the data is from. For example, in the context of the hospitality industry, “room” and “air-conditioning” occur quite frequently. Similarly, “spa” and “massage” occur more frequently than perhaps, say, spa and dining. Aspect-based sentiment analysis recognizes these oft-occurring aspect combinations and helps the sentiment analysis API platform that these co-occurrences are important from the point of view of the customer. Thus, when calculating the aggregate score of a hotel based on thousands of reviews, it gives a more concrete and targeted answer.
