Research Directions, Challenges and Issues in Opinion Mining

Rapid growth of Internet and availability of user reviews on the web for any product has provided a need for an effective system to analyze the web reviews. Such reviews are useful to some extent, promising both the customers and product manufacturers. For any popular product, the number of reviews can be in hundreds or even thousands. This creates difficulty for a customer to analyze them and make important decisions on whether to purchase the product or to not. Mining such product reviews or opinions is termed as opinion mining which is broadly classified into two main categories namely facts and opinions. Though there are several approaches for opinion mining, there remains a challenge to decide on the recommendation provided by the system. In this paper, we analyze the basics of opinion mining, challenges, pros & cons of past opinion mining systems and provide some directions for the future research work, focusing on the challenges and issues

Classifying product reviews from balanced datasets for Sentiment Analysis and Opinion Mining

The Online reviews provided for a product enables web user to make decisions appropriately. These reviews may be positive, negative or neutral in nature. Analyzing and classifying such product reviews have attracted reasonable interest. It has become quite hard to make decisions since we aren’t able to obtain the decisions quickly. Hence it is required to classify the reviews from balanced data sets for analysis and opinion mining of any applications. The reason for considering balanced data sets is that the decision will not be biased on the category of reviews considered. We have carried out investigations using similarity measures to categorize the reviews correctly. Experiments reveal that the reviews that were mixed in nature were able to be grouped correctly