Jantima Polpinij

An Ontology-Based Sentiment Classification Methodology for Online Consumer Reviews

This paper presents a method of ontology-based sentiment classification to classify and analyse online product reviews of consumers. We implement and experiment with a support vector machines text classification approach based on a lexical variable ontology. After testing, it could be demonstrated that the proposed method can provide more effectiveness for sentiment classification based on text content.

Content-Based Text Classifiers for Pornographic Web Filtering

Due to the flood of pornographic web sites on the internet, effective Web filtering systems are essential. Web filtering based on content has become one of the important techniques to handle and filter inappropriate information on the web. We examine two machine learning algorithms (support vector machines and Naive Bayes) for pornographic web filtering based on text content. We then focus initially on Thai-language and English-language web sites. In this paper, we aim to investigate whether machine learning algorithms are suitable for web sites classification. The empirical results show that the classifier based support vector machines are more effective for pornographic web filtering than Naive Bayes classifier after testing, especially an effectiveness for the over-blocking problem.

Business Rules Discovery from Process Design Repositories

Traditional process mining approaches focus on extracting process constraints or business rules from repositories of process instances. In this context, process designs or process models tend to be overlooked although they contain information that are valuable for the process of discovering business rules. This paper will propose an alternative approach to process mining in terms of using process designs as the mining resources. We propose a number of techniques for extracting business rules from repositories of business process designs or models, leveraging the well-known Apriori algorithm. Such business rules are then used as a prior knowledge for further analysing, verifying, and modifying process designs.

An ontology-based text processing approach for simplifying ambiguity of requirement specifications

In the last few years, several works in the literature of software engineering have addressed the problem of requirement management. A majority problem of software errors is introduced during the requirements phase because much of requirements specification is written in natural language format. As this, it is hard to identify consistencies because of too ambiguous for specification purpose. Therefore, this paper aims to propose a method for simplifying ambiguity of requirement specification documents through two concepts of ontology-based probabilistic text processing: Text classification and Text Filtering. Text classification is used to analyze and classify requirement specification having similar detail into the same class. This contributes to a better understanding of the impact of the requirements and to elaborate them. Meanwhile, text filters are used to leverage synopsis requirements in documents through probabilistic text classification technique. After testing by F-measure, the experimental results return a satisfactory accuracy. These demonstrate that our method may provide more effectiveness for simplifying ambiguity of requirement specifications.

Mining business rules from business process model repositories

– Business process has become the core assets of many organizations and it becomes increasing common for most medium to large organizations to have collections of hundreds or even thousands of business process models. The purpose of this paper is to explore an alternative dimension to process mining in which the objective is to extract process constraints (or business rules) as opposed to business process models. It also focusses on an alternative data set – process models as opposed to process instances (i.e. event logs). , – The authors present a new method of knowledge discovery to find business activity sequential patterns embedded in process model repositories. The extracted sequential patterns are considered as business rules. , – The authors find significant knowledge hidden in business processes model repositories. The hidden knowledge is considered as business rules. The business rules extracted from process models are significant and valid sequential correlations among business activities belonging to a particular organization. Such business rules represent business constraints that have been encoded in business process models. Experimental results have indicated the effectiveness and accuracy of the approach in extracting business rules from repositories of business process models. , – This research will assist organizations to extract business rules from their existing business process models. The discovered business rules are very important for any organization, where rules can be used to help organizations better achieve goals, remove obstacles to market growth, reduce costly mistakes, improve communication, comply with legal requirements, and increase customer loyalty. , – There has very been little work in mining business process models as opposed to an increasing number of very large collections of business process models. This work has filled this gap with the focus on extracting business rules.

Ontology-Based Text Classification for Filtering Cholangiocarcinoma Documents from PubMed

PubMed is a search engine used to access the MEDLINE database, which comprises the massive amounts of biomedical literature. This an make more difficult for accessing to find the relevant medical literature. Therefore, this problem has been challenging in this work. We present a solution to retrieve the most relevant biomedical literature relating to Cholangiocarcinoma in clinical trials from PubMed. The proposed methodology is called ontology-based text classification (On-TC). We provide an ontology used as a semantic tool. It is called Cancer Technical Term Net (CCT-Net). This ontology is intergrated to the methodology to support automatic semantic interpretation during text processing, especially in the case of synonyms or term variations.

An Automatic Elaborate Requirement Specification By Using Hierarchical Text Classification

Ambiguity is a major problem of software errors because much of the requirements specification is written in a natural language format. Therefore, it is hard to identify consistencies because this format is too ambiguous for specification purposes. This paper aims to propose a method for handling requirement specification documents which have a similar content to each other through a hierarchical text classification. The method consists of two main processes of classification: heavy classification and light classification. The heavy classification is to classify the requirement specification documents having similar content together. Meanwhile, light classification is to elaborate specification requirement documents by using the Euclidean distance. Finally, slimming down the number of requirements specification through hierarchical text classification classifying may yield a specification which is easier to understand. That means the proposed method is more effective for reducing and handling in the requirements specification.

Word2Vec Approach for Sentiment Classification Relating to Hotel Reviews

In general, the existing works in sentiment classification concentrate only the syntactic context of words. It always disregards the sentiment of text. This work addresses this issue by applying Word2Vec to learn sentiment specific words embedded in texts, and then the similar words will be grouped as a same concept (or class) with sentiment information. Simply speaking, the aim of this work is to introduce a new task similar to word expansion or word similarity task, where this approach helps to discover words sharing the same semantics automatically, and then it is able to separate positive or negative sentiment in the end. The proposed method is validated through sentiment classification based on the employing of Support Vector Machine (SVM) algorithm. This approach may enable a more efficient solution for sentiment analysis because it can help to reduce the inherent ambiguity in natural language.

Concept-Based Sentiment Analysis for Opinion Texts with Multiple-Languages

Today, millions of message posted daily contain opinions of users in a variety of languages, including emoticon. Sentiment analysis becomes a very difficult task, and the understanding and knowledge of the problem and its solution are still preliminary. Therefore, this work presents a new methodology, called Concept-based Sentiment Analysis (C-SA). The main mechanism of the C-SA is Msent-WordNet (Multilingual Sentiment WordNet), which is used to prove and increase the results accuracy of sentiment analysis. By using the Msent-WordNet, all words in opinion texts having similar sense or meaning will be denoted and considered as a same concept. Indeed, concept-level sentiment analysis aims to go beyond a mere word-level analysis of text and provide novel approaches to sentiment analysis that enables a more efficient solution from opinion text. This can help to reduce the inherent ambiguity and contextual nature of human languages. Finally, the proposed methodology is validated through sentiment classification.

Concept-Based Cross Language Retrieval for Thai Medicine Recipes

This work aims to present a new methodology to retrieve the documents relating to the traditional Thai medicine recipe that is translated from the ancient palm leaf manuscripts. This methodology is developed based on three main concepts: sematic data, latent search indexing (LSI), and cross language information retrieval (CLIR). Our methodology consists of four main processing steps. They are document indexing, document representation based on LSI, user’s query transformation, and document retrieval and ranking. After testing by the common performance measures for information retrieval system such as recall, precision, and F-measure, it would demonstrate that our methodology can achieve substantial improvements.