May 1, 2011

Interesting Research Topics in Opinion Mining and Sentiment Analysis

A friend once asked me "What do you guys do with opinions, you all seem to be working on the same thing?!". 

When talking about the area of opinion analysis in general, the common misconception is that it is all about trying to predict the polarity of a piece of 'opinion text' as being positive or negative. It is true that a lot of people have actually studied the task of sentiment polarity prediction, but the area of opinion analysis is actually much broader than just that. Actually, many of the tasks related to opinion analysis often go unnoticed due to lack of 'popularity'. Here are some interesting tasks related to opinion analysis which I have discovered over the course of reading related literature. I also have pointers to some highly cited papers within each subtask.


1. Subjectivity Detection

This task is about determining if a piece of text actually contains opinions or not (i.e. subjective expression or objective?). It is not so much about determining the polarity of the text itself (see task 2). Here are some notable papers related to this task:

2. Sentiment Prediction

This task is specifically about predicting the polarity of a piece of text usually positive or negative. People have studied sentiment prediction at the document level, sentence level and phrase level. This is an extremely popular task in the field of Opinion Analysis.
Some notable papers for this task can be found here.


3. Aspect Based Sentiment Summarization

This task goes beyond sentiment prediction. The goal is to provide a nice little sentiment summary at the feature or aspect level. For example for an iPhone you may have features like design, sound, screen, and etc. The goal is to provide a summary in the form of star ratings or scores on each of these features. So the task involves finding features and then discovering the sentiments for each feature. This task is now quite popular as it solves a practical need.
Some notable papers for this task can be found here.


4. Constrastive Viewpoint Summarization

This task is about trying to highlight contradiction in opinions where present. For example, some people may say the healthcare plan is a great idea and some may say that it is a failure waiting to happen. With contrastive viewpoints highlighted, people can get a better understanding of the opinions and under which condition it holds.
Some notable papers for this task can be found here.


5. Text Summarization for Opinions

Instead of generating structured summaries of opinions, another useful summary format is to generate textual summaries. For example, a few sentences summarizing the reviews of a product or a set of phrases acting as summaries.
Here are some related papers for this task:


6. Predicting Helpfulness of Online Comments/Reviews

Some comments or reviews may be more helpful or insightful compared to others. Instead of displaying these comments or user reviews in chronological order, sorting the reviews by its helpfulness would improve user productivity. This task thus aims at automatically predicting the helpfulness of user reviews instead of just relying on user votes.
Some related papers for this task can be found here.


7. Opinion-Based Entity Ranking

Opinion based-entity ranking is basically the task of ranking entities based on opinions. The query is essentially "preferences" for the entity. The results would be the likelihood of the entities matching those preferences. So the more opinions on the entities match the specified preferences, the higher the rank. This is very useful in finding for example attractions in a specific location that are considered to be "safe, close to the airport and child friendly". The first work to explore this  can be found here: http://kavita-ganesan.com/opinion-based-entity-ranking . There have been some variations and improvements over this work by different groups: 

Other Related Tasks

8. Product Feature Extraction

9. Opinion Retrieval

For more information about some of these tasks you can check these surveys.



sentiment analysis, opinion mining