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Letu2019s compare the competitive advantage of using aspect-based sentiment analysis (ABSA) over topic-based sentiment analysis (TBSA). For diligent market research, it is imperative to get a clear picture of what strategies will succeed and what wonu2019t - whether itu2019s for a new product launch, new service, product positioning, or even for a new target market.<br><br>
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Overview Let’s compare the competitive advantage of using aspect-based sentiment analysis (ABSA) over topic-based sentiment analysis (TBSA). For diligent market research, it is imperative to get a clear picture of what strategies will succeed and what won’t - whether it’s for a new product launch, new service, product positioning, or even for a new target market. Earlier in the day, the only resource available was through surveys, which were collected and studied manually. However, with social media at its peak, and review websites like TrustPilot, TripAdvisor, Yelp, Glassdoor and so many like these, data is just out there, ready and available to be harnessed for business intelligence. Sentiment mining thus gained can help companies with brand monitoring, crisis management, marketing campaigns, better customer service, better consumer engagement, improved work environment, and many other such functions.
What’s the Difference Between ABSA and TBSA? Aspect-based sentiment analysis is a customer-centric approach to understanding consumer emotion, while topic-based sentiment analysis is more product-centric. ABSA gives a comprehensive view of consumer likes and dislikes, so you can narrow down to the particulars of what aspects of your business can be improved. Topic analysis, on the other hand, gives a general view of the consumer’s overall feelings about his experience with your product or business.
What makes Aspect-based sentiment analysis better? While topic analysis only applies NER, aspect sentiment analysis employs both NER and semantic clustering. In this way, it automatically identifies key themes and topics in any data important to your organization and finds semantically similar words that can be grouped together to get an even more intricate comprehension of topics. This makes ABSA a more powerful tool than topic analysis in emotion mining for business insights. For better understanding, Named Entity Recognition is a machine learning task that identifies named entities like H&M, London, Kobe Bryant, Ferrari, and such, in the collected data and classifies them into predetermined categories like company names, regional locations, names of people, brands, etc.
When should you opt for ABSA? If you want a basic understanding of what irks or delights your customers, its topic analysis all the way. But if you want to dig deeper for improved product and service quality, ROI, and branding & advert campaigns, ABSA is your answer. This is a valid question for any company to ask, though. After all, you’ve decided to opt for an AI-based machine model to analyse data, invest in it, and hope to use the insights for optimization of resources and marketing functions. Now the question becomes - up to which level does topic analysis suit your purpose and when, as you up the ante on your game, is the right time to employ ABSA.
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