Samaneh Moghaddam

Opinion digger: an unsupervised opinion miner from unstructured product reviews

Mining customer reviews (opinion mining) has emerged as an interesting new research direction. Most of the reviewing websites such as Epinions.com provide some additional information on top of the review text and overall rating, including a set of predefined aspects and their ratings, and a rating guideline which shows the intended interpretation of the numerical ratings. However, the existing methods have ignored this additional information. We claim that using this information, which is freely available, along with the review text can effectively improve the accuracy of opinion mining. We propose an unsupervised method, called Opinion Digger, which extracts important aspects of a product and determines the overall consumers satisfaction for each, by estimating a rating in the range from 1 to 5. We demonstrate the improved effectiveness of our methods on a real life dataset that we crawled from Epinions.com.

The FLDA model for aspect-based opinion mining: addressing the cold start problem

Aspect-based opinion mining from online reviews has attracted a lot of attention recently. The main goal of all of the proposed methods is extracting aspects and/or estimating aspect ratings. Recent works, which are often based on Latent Dirichlet Allocation (LDA), consider both tasks simultaneously. These models are normally trained at the item level, i.e., a model is learned for each item separately. Learning a model per item is fine when the item has been reviewed extensively and has enough training data. However, in real-life data sets such as those from Epinions.com and Amazon.com more than 90% of items have less than 10 reviews, so-called cold start items. State-of-the-art LDA models for aspect-based opinion mining are trained at the item level and therefore perform poorly for cold start items due to the lack of sufficient training data. In this paper, we propose a probabilistic graphical model based on LDA, called Factorized LDA (FLDA), to address the cold start problem. The underlying assumption of FLDA is that aspects and ratings of a review are influenced not only by the item but also by the reviewer. It further assumes that both items and reviewers can be modeled by a set of latent factors which represent their aspect and rating distributions. Different from state-of-the-art LDA models, FLDA is trained at the category level and learns the latent factors using the reviews of all the items of a category, in particular the non cold start items, and uses them as prior for cold start items. Our experiments on three real-life data sets demonstrate the improved effectiveness of the FLDA model in terms of likelihood of the held-out test set. We also evaluate the accuracy of FLDA based on two application-oriented measures.

ETF: extended tensor factorization model for personalizing prediction of review helpfulness

Online reviews are valuable sources of information for a variety of decision-making processes such as purchasing products. As the number of online reviews is growing rapidly, it becomes increasingly difficult for users to identify those that are helpful. This has motivated research into the problem of identifying high quality and helpful reviews automatically. The current methods assume that the helpfulness of a review is independent from the readers of that review. However, we argue that the quality of a review may not be the same for different users. For example, a professional and an amateur photographer may rate the helpfulness of a review very differently. In this paper, we introduce the problem of predicting a personalized review quality for recommendation of helpful reviews. To address this problem, we propose a series of increasingly sophisticated probabilistic graphical models, based on Matrix Factorization and Tensor Factorization. We evaluate the proposed models using a database of 1.5 million reviews and more than 13 million quality ratings obtained from Epinions.com. The experiments demonstrate that the proposed latent factor models outperform the state-of-the art approaches using textual and social features. Finally, our experiments confirm that the helpfulness of a review is indeed not the same for all users and that there are some latent factors that affect a users evaluation of the review quality.

FeedbackTrust: using feedback effects in trust-based recommendation systems

With the advent of online social networks, the trust-based approach to recommendation has emerged which exploits the trust network among users and makes recommendations based on the ratings of trusted users in the network. In this paper, we introduce a two dimensional trust model which dynamically gets updated based on userss feedbacks, in contrast to static trust values in current trust models. Explorability measures the extent to which a user can rely on recommendations returned by the social network of a trusted user. Dependability represents the extent to which a users own ratings can be trusted by users trusting him directly and indirectly. We propose a method to learn the values of explorability and dependability from raw trust data and feedback expressed by users on the recommendations they receive. Positive feedback will increase the trust and negative feedback will decrease the trust among users. We performed an evaluation on the Epinions dataset, demonstrating that exploiting user feedback results in lower prediction error compared to existing trust-based and collaborative filtering approaches.

Review recommendation: personalized prediction of the quality of online reviews

The problem of identifying high quality and helpful reviews automatically has attracted many attention recently. Current methods assume that the helpfulness of a review is independent from the readers of that review. However, we argue that the quality of a review may not be the same for different users. In this paper, we employ latent factor models to address this problem. We evaluate the proposed models using a real life database from Epinions.com. The experiments demonstrate that the latent factor models outperform the state-of-the-art approaches and confirms that the helpfulness of a review is indeed not the same for all users.