Leveraging large amounts of opinions for decision making

Opinion Driven Decision Support System (ODSS) refers to the use of large amounts of online opinions to facilitate business and consumer decision making. The idea is to combine the strengths of search technologies with opinion mining and analysis tools to provide a synergistic decision making platform. The research and engineering problems related to developing such a system include :
  1. opinion acquisition
  2. opinion based search
  3. opinion summarization
  4. presentation of results
Opinions in this case can be aggregation of user reviews, blog comments, facebook status updates, Tweets and so on. Essentially any opinion containing texts on specific topics or entities qualify as candidates for building an ODSS platform. Here’s a description of some of the research and engineering problems towards developing an ODSS platform:

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. This special ranking problem is referred to as Opinion-Based Entity Ranking. Many of the existing opinion mining techniques can be potentially used for this new ranking task. I have explored information retrieval based techniques to specifically solve this ranking problem  and there has been a few follow-up works (from other groups) trying other approaches.

2. Opinion Summarization (i.e. Sentiment Analysis + Text 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. These are referred to as stuctured summaries. In the last few years, textual summaries of opinions have been gaining more and more popularity. Bing Liu’s Opinion Mining Tutorial covers some of these recent works or you can refer to this article point (5).

3. Opinion Acquisition (i.e. Opinion or Sentiment Crawling)

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. I recently explored a very simple method to collecting large amounts of opinions on arbitrary entities.

The idea of an Opinion Driven Decision Support (ODSS) was developed as part of my thesis. For more information on this please see Kavita’s thesis.

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