Christos Berberidis

Ontology-based sentiment analysis of twitter posts

The emergence of Web 2.0 has drastically altered the way users perceive the Internet, by improving information sharing, collaboration and interoperability. Micro-blogging is one of the most popular Web 2.0 applications and related services, like Twitter, have evolved into a practical means for sharing opinions on almost all aspects of everyday life. Consequently, micro-blogging web sites have since become rich data sources for opinion mining and sentiment analysis. Towards this direction, text-based sentiment classifiers often prove inefficient, since tweets typically do not consist of representative and syntactically consistent words, due to the imposed character limit. This paper proposes the deployment of original ontology-based techniques towards a more efficient sentiment analysis of Twitter posts. The novelty of the proposed approach is that posts are not simply characterized by a sentiment score, as is the case with machine learning-based classifiers, but instead receive a sentiment grade for each distinct notion in the post. Overall, our proposed architecture results in a more detailed analysis of post opinions regarding a specific topic.

On the Discovery of Weak Periodicities in Large Time Series

The search for weak periodic signals in time series data is an active topic of research. Given the fact that rarely a real world dataset is perfectly periodic, this paper approaches this problem in terms of data mining, trying to discover weak periodic signals in time series databases, when no period length is known in advance. In existing time series mining algorithms, the period length is user-specified. We propose an algorithm for finding approximate periodicities in large time series data, utilizing autocorrelation function and FFT. This algorithm is an extension to the partial periodicity detection algorithm presented in a previous paper of ours. We provide some mathematical background as well as experimental results.

Mining for Mutually Exclusive Items in Transaction Databases

Association rule mining is a popular task that involves the discovery of co-occurences of items in transaction databases. Several extensions of the traditional association rule mining model have been proposed so far; however, the problem of mining for mutually exclusive items has not been directly tackled yet. Such information could be useful in various cases (e.g., when the expression of a gene excludes the expression of another), or it can be used as a serious hint in order to reveal inherent taxonomical information. In this article, we address the problem of mining pairs of items, such that the presence of one excludes the other. First, we provide a concise review of the literature, then we define this problem, we propose a probability-based evaluation metric, and finally a mining algorithm that we test on transaction data.

Improving the accuracy of classifiers for the prediction of translation initiation sites in genomic sequences

The prediction of the Translation Initiation Site (TIS) in a genomic sequence is an important issue in biological research. Although several methods have been proposed to deal with this problem, there is a great potential for the improvement of the accuracy of these methods. Due to various reasons, including noise in the data as well as biological reasons, TIS prediction is still an open problem and definitely not a trivial task. In this paper we follow a three-step approach in order to increase TIS prediction accuracy. In the first step, we use a feature generation algorithm we developed. In the second step, all the candidate features, including some new ones generated by our algorithm, are ranked according to their impact to the accuracy of the prediction. Finally, in the third step, a classification model is built using a number of the top ranked features. We experiment with various feature sets, feature selection methods and classification algorithms, compare with alternative methods, draw important conclusions and propose improved models with respect to prediction accuracy.

MANTIS: A Data Mining Methodology for Effective Translation Initiation Site Prediction

The prediction of the translation initiation site in a genomic sequence with the highest possible accuracy is an important problem that still has to be investigated by the research community. Current approaches perform quite well, however there is still room for a more general framework for the researchers who want to follow an effective and reliable methodology. We developed a prediction methodology that combines ad hoc as well as discovered knowledge in order to significantly increase the achieved accuracy reliably. Our methodology is modular and consists of three major decision components: a consensus component, a coding region classification component and a novel ATG location-based component that allows for the utilization of the advantages of the popular ribosome scanning model while overcoming its limitations. All three of them are combined into a meta-classification system, using stacked generalization, in a highly effective prediction framework. We performed extensive comparative experiments on four different datasets, showing that the increase in terms of accuracy and adjusted accuracy is not only statistically significant, but also the highest reported.

A novel data mining approach for the accurate prediction of translation initiation sites

In an mRNA sequence, the prediction of the exact codon where the process of translation starts (Translation Initiation Site – TIS) is a particularly important problem. So far it has been tackled by several researchers that apply various statistical and machine learning techniques, achieving high accuracy levels, often over 90%. In this paper we propose a mahine learning approach that can further improve the prediction accuracy. First, we provide a concise review of the literature in this field. Then we propose a novel feature set. We perform extensive experiments on a publicly available, real world dataset for various vertebrate organisms using a variety of novel features and classification setups. We evaluate our results and compare them with a reference study and show that our approach that involves new features and a combination of the Ribosome Scanning Model with a meta-classifier shows higher accuracy in most cases.

Deploying a semantically-enabled content management system in a state university

Public institutes often face the challenge of managing vast volumes of administrative documents, a need that is often met via Content Management Systems (CMSs). CMSs offer various advantages, like separation of data structure from presentation and variety in user roles, but also present certain disadvantages, like inefficient keyword-based search facilities. The new generation of content management solutions imports the notion of semantics and is based on Semantic Web technologies, such as metadata and ontologies. The benefits include semantic interoperability, competitive advantages and dramatic cost reduction. In this paper a leading Enterprise CMS is extended with semantic capabilities for automatically importing and exporting ontologies. This functionality enables reuse of repository content, semantically-enabled search and interoperability with third-party applications. The extended system is deployed in semantically managing the large volumes of documents for a state university.

StackTIS: A stacked generalization approach for effective prediction of translation initiation sites

The prediction of the translation initiation site in an mRNA or cDNA sequence is an essential step in gene prediction and an open research problem in bioinformatics. Although recent approaches perform well, more effective and reliable methodologies are solicited. We developed an adaptable data mining method, called StackTIS, which is modular and consists of three prediction components that are combined into a meta-classification system, using stacked generalization, in a highly effective framework. We performed extensive experiments on sequences of two diverse eukaryotic organisms (Homo sapiens and Oryza sativa), indicating that StackTIS achieves statistically significant improvement in performance.

Mining for Contiguous Frequent Itemsets in Transaction Databases

Mining a transaction database for association rules is a particularly popular data mining task, which involves the search for frequent co-occurrences among items. One of the problems often encountered is the large number of weak rules extracted. Item taxonomies, when available, can be used to reduce them to a more usable volume. In this paper we introduce a new data mining paradigm, which involves the discovery of contiguous frequent itemsets. We formulate the problem of mining contiguous frequent itemsets in a transaction database and we present a level-wise algorithm for finding these itemsets. Contiguous frequent itemsets may contain important knowledge about the dataset, that can not be exposed by the use of classic association rule mining approaches. This knowledge may well include serious hints for the generation of a taxonomy for all or part of the items.

DETECTION AND PREDICTION OF RARE EVENTS IN TRANSACTION DATABASES

Rare events analysis is an area that includes methods for the detection and prediction of events, e.g. a network intrusion or an engine failure, that occur infrequently and have some impact to the ...