Interview: Kavita Ganesan, FindiLike on Building Decision Support Systems based on User Opinions
We discuss the founding story of FindiLike, Opinion-driven Decision Support Systems (ODSS), challenges in analyzing user opinions, future of Sentiment Analysis, favorite books and more.

She received her Ph.D. from the University of Illinois at Urbana-Champaign where she finished a dissertation on opinion-driven decision support system proposing a suite of novel and highly general algorithms for online review crawling, abstractive and concise opinion summarization, and opinion-based entity ranking. She is very passionate about using research in practice and with that focuses on developing techniques that are general and scalable.
Here is my interview with her:
Anmol Rajpurohit: Q1. What inspired you to launch FindiLike? When was the first time that you thought about it? How did your recent PhD contribute to it?

AR: Q2. How would you define an Opinion-driven Decision Support System (ODSS)? What are the kind of research problems that ODSS encompasses?
KG: An Opinion-driven Decision Support System is basically a platform consisting of tools and technologies that would facilitate users and businesses to leverage opinions more efficiently for all sorts of decision making tasks. For example, for a user, this can be a decision making task on which product to purchase based on all available opinions. And for a business, this can be what problems of their very own product to fix based on opinions of other users. To facilitate such a platform there is actually a multitude of interesting research problems ranging from data mining problems to human computer interaction problems. Example of research problems:
Opinion Summarization
One of the easiest ways to analyze the abundance of unstructured opinions is through the summarization of all these opinions. There is a whole range of methods to actually summarize opinions with each method having its pros and cons. A lot of details on the different methods for structured summarization (for eg, opinion through rating scales) can be found in the survey paper by Kim et. al 2011. Then you also have unstructured summaries where these are basically textual summaries, trying to summarize the key opinions in text. In recent years, researchers have actually been looking into abstractive micro-summarization (micropinion) format rather than sentence extraction methods, where you try to generate concise, abstractive and readable summaries on key opinions. The reason for this is because full sentences can become verbose and may not be suitable for hand-held devices. The example below shows what a micropinion summary looks like when run on reviews of Acura 2007. This was run using a variant algorithm based on several research projects: Micropinion-generation and Opinosis.

More examples: Opinion-Driven Search, Opinion Acquisition (OpinoFetch)
AR: Q3. What are the biggest challenges in mining the opinions scattered all across the web (including social media) and making sense out of it?
KG: Based on my experience, the biggest challenge with all these scattered opinions is noise and duplicates. Since opinions can be highly redundant, we have the benefit of volume to actually surface important opinions for analysis. Along with this, we often times would have “noise” and duplicates

AR: Q4. How would you differentiate FindiLike from the other opinion mining and sentiment analysis tools?
KG: Unlike typical sentiment analysis tools that tag text to contain positive or negative sentiments or full-scale market research type of sentiment analysis applications, the goal of FindiLike is to provide pre-requisite

AR: Q5. What do you personally think about the future of Sentiment Analysis? Your predictions?

Sentiment analysis would go beyond just the “positive” or “negative” that it is currently thought to be. What is going to really matter eventually is what actually people have said which provides a lot more information and insights rather than if something was positive or negative.
For example, "iPhone 5s design: positive" is not as informative as “the iphone 5s is sleek, fits easily in the pocket and has a beautiful interface”.
Also, in the industry, the current focus of sentiment analysis is primarily restricted to opinions within social media content. However, opinions are far more ubiquitous. You have an abundance of opinions in the form of user reviews which actually contain a lot of details, then you have opinions within user comments (e.g. comments on articles, videos, etc.), you also have opinions within forums. So the value would soon come from all these other sources and not just social media content.
AR: Q6. What is the best advice you have got in your career?
KG: Be brave, take risks! You never know where a new adventure would take you. It may take you to a better place than you envisioned or to a place that you have always dreamt about.
AR: Q7. What are your favorite books or blogs on Data Science?

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