Research on feature-based opinion mining using topic maps

Xia, Linlin; Wang, Zhongyi; Chen, Chen; Zhai, Shanshan

doi:10.1108/el-11-2014-0197

Cited by 3 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the P2P environment breaks the traditional client-server architecture, weakens the server role, changes the shared resources storage location from “center” to “marginal” and distributes shared knowledge resources over every participating peer. As a typical application of Web 2.0, the P2P environment especially shows the basic tenets of Web 2.0, which are freedom, equality and interconnection (Xia et al , 2016). Moreover, compared with the centralized knowledge-sharing environment, the P2P environment is more similar to people’s actual process of accessing, communicating and exchanging knowledge resources.…”

Section: Introductionmentioning

confidence: 99%

Construction of personal knowledge maps for a peer-to-peer information-sharing environment

Qin

Zhao

Mou

et al. 2018

View full text Add to dashboard Cite

Purpose Browsing knowledge documents in a peer-to-peer (P2P) environment is difficult because knowledge documents in such an environment are large in quantity and distributed over different peers who organize the documents according to their own views. This paper aims to propose a method for constructing a personal knowledge map for a peer to facilitate knowledge browsing and alleviate information overload in P2P environments. Design/methodology/approach The research presents a method for constructing a personal knowledge map. The method adopts an ontology-concept-tree-based classification algorithm to recognize a peer’s personal knowledge structure and construct a personal knowledge map, and uses a self-organizing map algorithm to cluster and visualize the knowledge documents. The correctness of the created knowledge map is evaluated with a collection of abstracts of academic papers. Findings The method for constructing a personal knowledge map is the main finding of this research. The evaluation shows that the created knowledge map is good in quality. Research limitations/implications The proposed method provides a way for P2P platforms to understand their users’ knowledge background, as well as to improve the P2P platform environment. However, the proposed method will not help a peer when he has nothing in his individual knowledge document repository (i.e. the “cold start” problem). The method also requires a relatively good ontology base for a P2P document sharing system to use the method effectively. Originality/value It is novel that the proposed method organizes the knowledge documents related to a peer’s knowledge background into a personal knowledge map. Moreover, the created knowledge map combines the advantages of a hierarchical display and a map display. It has values for a distributed P2P environment to facilitate users’ knowledge browsing and to alleviate information overload.

show abstract

Section: Introductionmentioning

confidence: 99%

Construction of personal knowledge maps for a peer-to-peer information-sharing environment

Qin

Zhao

Mou

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The experimental results revealed that the feature-based method can improve the accuracy of the Opinion mining by up to12% (Xia, Wang, Chen, & Zhai, 2016).…”

Section: Related Studiesmentioning

confidence: 99%

“…Vol.9, No.3, 2019 Weights applied to the terms are crucial to the accuracy of the classification process. Some of the commonly used term weighting method of feature selection in text classification include TF, TF-IDF, and Boolean (Xia, Wang, Chen, & Zhai, 2016). Boolean weighting is the simplest way to weighting term of feature vectors by assigning them 0 or 1.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Techniques and Classification Accuracy of Supervised Machine Learning in Text Mining

2019

JIEA

View full text Add to dashboard Cite

Text mining is a special case of data mining which explore unstructured or semi-structured text documents, to establish valuable patterns and rules that indicate trends and significant features about specific topics. Text mining has been in pattern recognition, predictive studies, sentiment analysis and statistical theories in many areas of research, medicine, financial analysis, social life analysis, and business intelligence. Text mining uses concept of natural language processing and machine learning. Machine learning algorithms have been used and reported to give great results, but their performance of machine learning algorithms is affected by factors such as dataset domain, number of classes, length of the corpus, and feature selection techniques used. Redundant attribute affects the performance of the classification algorithm, but this can be reduced by using different feature selection techniques and dimensionality reduction techniques. Feature selection is a data preprocessing step that chooses a subset of input variable while eliminating features with little or no predictive information. Feature selection techniques are Information gain, Term Frequency, Term Frequency-Inverse document frequency, Mutual Information, and Chi-Square, which can use a filters, wrappers, or embedded approaches. To get the most value from machine learning, pairing the best algorithms with the right tools and processes is necessary. Little research has been done on the effect of feature selection techniques on classification accuracy for pairing of these algorithms with the best feature selection techniques for optimal results. In this research, a text classification experiment was conducted using incident management dataset, where incidents were classified into their resolver groups. Support vector machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB) and Decision tree (DT) machine learning algorithms were examined. Filtering approach was used on the feature selection techniques, with different ranking indices applied for optimal feature set and classification accuracy results analyzed. The classification accuracy results obtained using TF were, 88% for SVM, 70% for NB, 79% for Decision tree, and KNN had 55%, while Boolean registered 90%, 83%, 82% and 75%, for SVM, NB, DT, and KNN respectively. TF-IDF, had 91%, 83%, 76%, and 56% for SVM, NB, DT, and KNN respectively. The results showed that algorithm performance is affected by feature selection technique applied. SVM performed best, followed by DT, KNN and finally NB. In conclusion, presence of noisy data leads to poor learning performance and increases the computational time. The classifiers performed differently depending on the feature selection technique applied. For optimal results, the classifier that performed best together with the feature selection technique with the best feature subset should be applied for all types of data for accurate classification performance.

show abstract

“…The word ontology is derived from the field of philosophy, with the important specification that it has great potential to help in the organizing and managing of knowledge (Santoso et al, 2011). Ontology can be used to present knowledge hidden within a mountain of data; it can integrate domain knowledge and demonstrate its consistency (Jiang et al, 2005;Xia et al, 2016). In recent years, ontology has played an increasingly important role in textual analysis and information exchange between different areas (Zhai and Massung, 2016).…”

Section: Ontology and Topic Mapsmentioning

confidence: 99%

“…In recent years, topic maps, which have been widely used in the construction of conceptual architecture, have received growing attention (Santoso et al, 2011). They can transform implicit knowledge into explicit knowledge, providing benefits to many applicationsfor example, information retrieval systems (Lee et al, 2007;Xia et al, 2016) and Web search engines (Al-Rajebah and Al-Khalifa, 2010;Chiu and Pan, 2014). There have been many studies concerning topic map construction (Du et al, 2009;Santoso et al, 2011;Yao et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Considering social information in constructing research topic maps

Wang

Chiang

Huang

2018

View full text Add to dashboard Cite

Purpose In academic work, it is important to identify a specific domain of research. Many researchers may look to conference issues to determine interesting or new topics. Furthermore, conference issues can help researchers identify current research trends in their field and learn about cutting-edge developments in their area of specialization. However, so much conference information is published online that it can be difficult to navigate and analyze in a meaningful or productive way. Hence, the use of knowledge management (KM) could be a way to resolve these issues. In KM, ontology is widely adopted, but most ontology construction methods do not consider social information between target users. Therefore, this study aims to propose a novel method of constructing research topic maps using an open directory project (ODP) and social information. Design/methodology/approach The approach is to incorporate conference information (i.e. title, keywords and abstract) as sources and to consider the ways in which social information automatically produces research topic maps. The methodology can be divided into four modules: data collection, element extraction, social information analysis and visualization. The data collection module collects the required conference data from the internet and performs pre-processing. Then, the element extraction module extracts topics, associations and other basic elements of topic maps while considering social information. Finally, the results will be shown in the visualization module for researchers to browse and search. Findings The results of this study propose three main findings. First, creating topic maps with the ODP category information can help capture a richer set of classification associations. Second, social information should be considered when constructing topic maps. This study includes the relationship among different authors and topics to support information in social networks. By considering social information, such as co-authorship/collaborator, this method helps researchers find research topics that are unfamiliar but interesting or potential cooperative opportunities in the future. Third, this study presents topic maps that show a clear and simple pathway in interested domain knowledge. Research limitations implications First, this study analyzes and collects conference information, including the titles, keywords and abstracts of conference papers, so the data set must include all of the abovementioned information. Second, social information only analyzes co-authorship associations (collabship associations); other social information could be extracted in the future study. Third, this study only analyzes the associations between topics. The intensity of associations is not discussed in the study. Originality/value The study will have a great impact on learned societies because it bridges the gap between theory and practice. The study is useful for researchers who want to know which conferences are related to their research. Moreover, social networks can help researchers expand and diversify their research.

show abstract

Research on feature-based opinion mining using topic maps

Cited by 3 publications

References 25 publications

Construction of personal knowledge maps for a peer-to-peer information-sharing environment

Construction of personal knowledge maps for a peer-to-peer information-sharing environment

Feature Selection Techniques and Classification Accuracy of Supervised Machine Learning in Text Mining

Considering social information in constructing research topic maps

Contact Info

Product

Resources

About