Opinion Driven Decision Support is a term that I coined as part of my Ph.D. thesis refering to the use of large amounts of online opinions to facilitate business and consumer decision making. The idea in my thesis is to combine the strengths of search technologies with opinion mining and analysis tools to provide a powerful decision making platform. This special platform encompasses research problems related to opinion acquisition, opinion based search and opinion summarization. Opinions in this case can be aggregation of user reviews, blog comments, facebook status updates and so on. Essentially any opinion containing texts on specific topics or entities qualify as candidates for building an Opinion Driven Decision Support System. The building blocks towards devloping an Opinion-Driven Decision Support System are as follows:
Components of an ODSS
1. Search Capabilities Based on Opinions
The goal of opinion-based search is to help users find entities of interest based on their key requirements. Since a user is often interested in choosing an entity based on opinions on that entity, a system that ranks entities based on a user’s personal preferences would provide a more direct support for a user’s decision-making task. For example, in the case of finding hotels at a destination, a user may only want to consider hotels where other people thought was clean. By finding and ranking hotels based on how well it satisfies such a requirement would significantly reduce the number of entities in consideration, facilitating decision making. Unlike traditional search, the query in this case is a set of preferences and the results is a set of entities that match these preferences. The challenge is to accurately match the user’s preferences with existing opinions in order to recommend the best entities. Existing opinion mining techniques can be used for this purpose as well as information retrieval based techniques such as the one I have explored Opinion-Based Entity Ranking.
2. Opinion Summarization
Opinion summaries play a critical role in helping users better analyze entities in consideration (e.g. product, physician, cars, politican). Users are often looking out for major concerns or advantages in selecting a specific entity. Thus, a summary that can quickly highlight the key opinions about the entity would significantly help exploration of entities and aid decision making. The field of opinion summarization has been long explored with most techniques being focused on generating structured summaries on a fixed set of topics. In the last few years, textual summaries of opinions have been gaining more and more popularity. In my thesis, I focus on several textual summarization approaches:
3. Opinion Acquisition
To support accurate search and analysis based on opinions, opinionated content is imperative. Relying on opinions from just one specific source not only makes the information unreliable, but also incomplete due to variations in opinions as well as potential bias present in a specific source. Although many applications rely on large amounts of opinions, there has been very limited work on collecting and integrating a complete set of opinions.
- See my article on “Interesting Research Topics in Opinion Mining and Sentiment Analysis“
Software Related to ODSS
- Opinosis Textual Opinion Summarizer (Command Line Jar File)
- Opinosis REST API (Web API)
- FindiLike Demo on Search Based on Opinions