Opinionated social media such as product reviews are now widely used by individuals and organizations for their decision making. However, due to the reason of profit or fame, people try to game the system by opinion spamming (e.g., writing fake reviews) to promote or to demote some target products. In recent years, fake review detection has attracted significant attention from both the business and research communities. However, due to the difficulty of human labeling needed for supervised learning and evaluation, the problem remains to be highly challenging. This work proposes a novel angle to the problem by modeling spamicity as latent. An unsupervised model, called Author Spamicity Model (ASM), is proposed. It works in the Bayesian setting, which facilitates modeling spamicity of authors as latent and allows us to exploit various observed behavioral footprints of reviewers. The intuition is that opinion spammers have different behavioral distributions than non-spammers. This creates a distributional divergence between the latent population distributions of two clusters: spammers and non-spammers. Model inference results in learning the population distributions of the two clusters. Several extensions of ASM are also considered leveraging from different priors. Experiments on a real-life Amazon review dataset demonstrate the effectiveness of the proposed models which significantly outperform the state-of-the-art competitors.

Online product reviews have become an important source of user opinions. Due to profit or fame, imposters have been writing deceptive or fake reviews to promote and/or to demote some target products or services. Such imposters are called review spammers. In the past few years, several approaches have been proposed to deal with the problem. In this work, we take a different approach, which exploits the burstiness nature of reviews to identify review spammers. Bursts of reviews can be either due to sudden popularity of products or spam attacks. Reviewers and reviews appearing in a burst are often related in the sense that spammers tend to work with other spammers and genuine reviewers tend to appear together with other genuine reviewers. This paves the way for us to build a network of reviewers appearing in different bursts. We then model reviewers and their co-occurrence in bursts as a Markov Random Field (MRF), and employ the Loopy Belief Propagation (LBP) method to infer whether a reviewer is a spammer or not in the graph. We also propose several features and employ feature induced message passing in the LBP framework for network inference. We further propose a novel evaluation method to evaluate the detected spammers automatically using supervised classification of their reviews. Additionally, we employ domain experts to perform a human evaluation of the identified spammers and non-spammers. Both the classification result and human evaluation result show that the proposed method outperforms strong baselines, which demonstrate the effectiveness of the method.

Topic models have been widely used to discover latent topics in text documents. However, they may produce topics that are not interpretable for an application. Researchers have proposed to incorporate prior domain knowledge into topic models to help produce coherent topics. The knowledge used in existing models is typically domain dependent and assumed to be correct. However, one key weakness of this knowledge-based approach is that it requires the user to know the domain very well and to be able to provide knowledge suitable for the domain, which is not always the case because in most real-life applications, the user wants to find what they do not know. In this paper, we propose a framework to leverage the general knowledge in topic models. Such knowledge is domain independent. Specifically, we use one form of general knowledge, i.e., lexical semantic relations of words such as synonyms, antonyms and adjective attributes, to help produce more coherent topics. However, there is a major obstacle, i.e., a word can have multiple meanings/senses and each meaning often has a different set of synonyms and antonyms. Not every meaning is suitable or correct for a domain. Wrong knowledge can result in poor quality topics. To deal with wrong knowledge, we propose a new model, called GK-LDA, which is able to effectively exploit the knowledge of lexical relations in dictionaries. To the best of our knowledge, GK-LDA is the first such model that can incorporate the domain independent knowledge. Our experiments using online product reviews show that GK-LDA performs significantly better than existing state-of-the-art models.

Aspect extraction is one of the key tasks in sentiment analysis. In recent years, statistical models have been used for the task. However, such models without any domain knowledge often produce aspects that are not interpretable in applications. To tackle the issue, some knowledge-based topic models have been proposed, which allow the user to input some prior domain knowledge to generate coherent aspects. However, existing knowledge-based topic models have several major shortcomings, e.g., little work has been done to incorporate the cannot-link type of knowledge or to automatically adjust the number of topics based on domain knowledge. This paper proposes a more advanced topic model, called MC-LDA (LDA with m-set and c-set), to address these problems, which is based on an Extended generalized Polya urn (E-GPU) model (which is also proposed in this paper). Experiments on real-life product reviews from a variety of domains show that MCLDA outperforms the existing state-of-the-art models markedly.

In this paper, we propose a novel opinion summarization problem called compact explanatory opinion summarization (CEOS) which aims to extract within-sentence explanatory text segments from input opinionated texts to help users better understand the detailed reasons of sentiments. We propose and study general methods for identifying candidate boundaries and scoring the explanatoriness of text segments using Hidden Markov Models. We create new data sets and use a new evaluation measure to evaluate CEOS. Experimental results show that the proposed methods are effective for generating an explanatory opinion summary, outperforming a standard text summarization method.

This paper proposes to study the problem of identifying intention posts in online discussion forums. For example, in a discussion forum, a user wrote “I plan to buy a camera,” which indicates a buying intention. This intention can be easily exploited by advertisers. To the best of our knowledge, there is still no reported study of this problem. Our research found that this problem is particularly suited to transfer learning because in different domains, people express the same intention in similar ways. We then propose a new transfer learning method which, unlike a general transfer learning algorithm, exploits several special characteristics of the problem. Experimental results show that the proposed method outperforms several strong baselines, including supervised learning in the target domain and a recent transfer learning method.

We introduce a novel sentence ranking problem called explanatory sentence extraction (ESE) which aims to rank sentences in opinionated text based on their usefulness for helping users understand the detailed reasons of sentiments (i.e., "explanatoriness"). We propose and study several general methods for scoring the explanatoriness of a sentence. We create new data sets and propose a new measure for evaluation. Experiment results show that the proposed methods are effective, outperforming a state of the art sentence ranking method for standard text summarization.

Topic models have been widely used to identify topics in text corpora. It is also known that purely unsupervised models often result in topics that are not comprehensible in applications. In recent years, a number of knowledge-based models have been proposed, which allow the user to input prior knowledge of the domain to produce more coherent and meaningful topics. In this paper, we go one step further to study how the prior knowledge from other domains can be exploited to help topic modeling in the new domain. This problem setting is important from both the application and the learning perspectives because knowledge is inherently accumulative. We human beings gain knowledge gradually and use the old knowledge to help solve new problems. To achieve this objective, existing models have some major difficulties. In this paper, we propose a novel knowledge-based model, called MDK-LDA, which is capable of using prior knowledge from multiple domains. Our evaluation results will demonstrate its effectiveness.

The rapid proliferation of online forums has made it possible for people to share their intentions, wishes and experiences by posting comments with the aim of getting advice from other members of the forum. Extracting intentions from these comments provides valuable insight for companies who can exploit it to get a competitive edge. However, given the very large amount of this kind of online comments, manually extracting intentions is impractical, time consuming and expensive. Companies need tools that analyze the text to extract intentions and details about them. In this paper we propose to demo one such tool called Intention Insider which has been developed at HP Labs in close collaboration with business units and a few selected customers. The tool can ingest content from online forums or from uploaded files and quickly sift through very large amounts of comments to extract intention information. This information is loaded into a data warehouse to be correlated with other structured data and queried to produce interactive reports and dynamic visualizations that facilitate its exploration at detailed and aggregate levels.

ABSTRACT One of the central problems of opinion mining is to extract aspects of entities or topics that have been evaluated in an opinion sentence or document. Much of the existing research focused on extracting explicit aspects which are nouns and nouns phrases that have ap‐ peared in sentences, e.g., price in The price of this bike is very high. (owever, in many cas‐ es, people do not explicitly mention an aspect in a sentence, but the aspect is implied, e.g., This bike is expensive, where expensive indicates the price aspect of the bike. Although there are some existing works dealing with the problem, they all used the corpus‐based approach, which has several shortcomings. )n this paper, we propose a dictionary‐based approach to address these shortcomings. We formulate the problem as collective classifica‐ tion. Experimental results show that the proposed approach is effective and produces sig‐ nificantly better results than strong baselines based on traditional supervised classification.