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Text mining have gained great momentum in recent years, with user-generated content becoming widely available. One key use is comment mining, with much attention being given to sentiment analysis and opinion mining. An essential step in the process of comment mining is text pre-processing; a step in which each linguistic term is assigned with a weight that commonly increases with its appearance in the studied text, yet is offset by the frequency of the term in the domain of interest. A common practice is to use the well-known tf-idf formula to compute these weights. This paper reveals the bias introduced by between-participants’ discourse to the study of comments in social media, and proposes an adjustment. We find that content extracted from discourse is often highly correlated, resulting in dependency structures between observations in the study, thus introducing a statistical bias. Ignoring this bias can manifest in a non-robust analysis at best and can lead to an entirely wrong conclusion at worst. We propose an adjustment to tf-idf that accounts for this bias. We illustrate the effects of both the bias and correction with with seven Facebook fan pages data, covering different domains, including news, finance, politics, sport, shopping, and entertainment. [ABSTRACT FROM AUTHOR]

In any competitive business, success is based on the ability to make an item more appealing to customers than the competition. A number of questions arise in the context of this task: how do we formalize and quantify the competitiveness between two items? Who are the main competitors of a given item? What are the features of an item that most affect its competitiveness? Despite the impact and relevance of this problem to many domains, only a limited amount of work has been devoted toward an effective solution. In this paper, we present a formal definition of the competitiveness between two items, based on the market segments that they can both cover. Our evaluation of competitiveness utilizes customer reviews, an abundant source of information that is available in a wide range of domains. We present efficient methods for evaluating competitiveness in large review datasets and address the natural problem of finding the top-k competitors of a given item. Finally, we evaluate the quality of our results and the scalability of our approach using multiple datasets from different domains. [ABSTRACT FROM PUBLISHER]

In the era of “big data”, one of the key challenges is to analyze large amounts of data collected in meaningful and scalable ways. The field of process mining is concerned with the analysis of data that is of a particular nature, namely data that results from the execution of business processes. The analysis of such data can be negatively influenced by the presence of outliers, which reflect infrequent behavior or “noise”. In process discovery, where the objective is to automatically extract a process model from the data, this may result in rarely travelled pathways that clutter the process model. This paper presents an automated technique to the removal of infrequent behavior from event logs. The proposed technique is evaluated in detail and it is shown that its application in conjunction with certain existing process discovery algorithms significantly improves the quality of the discovered process models and that it scales well to large datasets. [ABSTRACT FROM PUBLISHER]

For various entering and transmission issues raised by human or system, missing events often occur in event data, which record execution logs of business processes. Without recovering the missing events, applications such as provenance analysis or complex event processing built upon event data are not reliable. Following the minimum change discipline in improving data quality, it is also rational to find a recovery that minimally differs from the original data. Existing recovery approaches fall short of efficiency owing to enumerating and searching over all of the possible sequences of events. In this paper, we study the efficient techniques for recovering missing events. According to our theoretical results, the recovery problem appears to be NP-hard. Nevertheless, advanced indexing, pruning techniques are developed to further improve the recovery efficiency. The experimental results demonstrate that our minimum recovery approach achieves high accuracy, and significantly outperforms the state-of-the-art technique for up to five orders of magnitudes improvement in time performance. [ABSTRACT FROM AUTHOR]