Identifying e-Commerce in Enterprises by means of Text Mining and Classification Algorithms

Monitoring specific features of the enterprises, for example, the adoption of e-commerce, is an important and basic task for several economic activities. This type of information is usually obtained by means of surveys, which are costly due to the amount of personnel involved in the task. An automatic detection of this information would allow consistent savings. This can actually be performed by relying on computer engineering, since in general this information is publicly available on-line through the corporate websites. This work describes how to convert the detection of e-commerce into a supervised classification problem, where each record is obtained from the automatic analysis of one corporate website, and the class is the presence or the absence of e-commerce facilities. The automatic generation of similar data records requires the use of several Text Mining phases; in particular we compare six strategies based on the selection of best words and best n-grams. After this, we classify the obtained dataset by means of four classification algorithms: Support Vector Machines; Random Forest; Statistical and Logical Analysis of Data; Logistic Classifier. This turns out to be a difficult case of classification problem. However, after a careful design and set-up of the whole procedure, the results on a practical case of Italian enterprises are encouraging

Learning Dynamic Classes of Events using Stacked Multilayer Perceptron Networks

People often use a web search engine to find information about events of interest, for example, sport competitions, political elections, festivals and entertainment news. In this paper, we study a problem of detecting event-related queries, which is the first step before selecting a suitable time-aware retrieval model. In general, event-related information needs can be observed in query streams through various temporal patterns of user search behavior, e.g., spiky peaks for popular events, and periodicities for repetitive events. However, it is also common that users search for non-popular events, which may not exhibit temporal variations in query streams, e.g., past events recently occurred, historical events triggered by anniversaries or similar events, and future events anticipated to happen. To address the challenge of detecting dynamic classes of events, we propose a novel deep learning model to classify a given query into a predetermined set of multiple event types. Our proposed model, a Stacked Multilayer Perceptron (S-MLP) network, consists of multilayer perceptron used as a basic learning unit. We assemble stacked units to further learn complex relationships between neutrons in successive layers. To evaluate our proposed model, we conduct experiments using real-world queries and a set of manually created ground truth. Preliminary results have shown that our proposed deep learning model outperforms the state-of-the-art classification models significantly.Comment: Neu-IR '16 SIGIR Workshop on Neural Information Retrieval, 6 pages, 4 figure

Toward Optimal Feature Selection in Naive Bayes for Text Categorization

Automated feature selection is important for text categorization to reduce the feature size and to speed up the learning process of classifiers. In this paper, we present a novel and efficient feature selection framework based on the Information Theory, which aims to rank the features with their discriminative capacity for classification. We first revisit two information measures: Kullback-Leibler divergence and Jeffreys divergence for binary hypothesis testing, and analyze their asymptotic properties relating to type I and type II errors of a Bayesian classifier. We then introduce a new divergence measure, called Jeffreys-Multi-Hypothesis (JMH) divergence, to measure multi-distribution divergence for multi-class classification. Based on the JMH-divergence, we develop two efficient feature selection methods, termed maximum discrimination ($MD$) and $MD-\chi^2$ methods, for text categorization. The promising results of extensive experiments demonstrate the effectiveness of the proposed approaches.Comment: This paper has been submitted to the IEEE Trans. Knowledge and Data Engineering. 14 pages, 5 figure

Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research