• The Journal of Information Systems
• /
• v.27 no.1
• /
• pp.89-110
• /
• 2018

Purpose There is much information in customer reviews, but finding key information in many texts is not easy. Business decision makers need a model to solve this problem. In this study we propose a multi-topic sentiment analysis approach using Latent Dirichlet Allocation (LDA) for user-generated contents (UGC). Design/methodology/approach In this paper, we collected a total of 104,039 hotel reviews in seven of the world's top tourist destinations from TripAdvisor (www.tripadvisor.com) and extracted 30 topics related to the hotel from all customer reviews using the LDA model. Six major dimensions (value, cleanliness, rooms, service, location, and sleep quality) were selected from the 30 extracted topics. To analyze data, we employed R language. Findings This study contributes to propose a lexicon-based sentiment analysis approach for the keywords-embedded sentences related to the six dimensions within a review. The performance of the proposed model was evaluated by comparing the sentiment analysis results of each topic with the real attribute ratings provided by the platform. The results show its outperformance, with a high ratio of accuracy and recall. Through our proposed model, it is expected to analyze the customers' sentiments over different topics for those reviews with an absence of the detailed attribute ratings.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.43-56
• /
• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• Language and Information
• /
• v.19 no.2
• /
• pp.19-36
• /
• 2015

The aim of this paper is to investigate the usage of Korean markers -(n)un and -i/ka in editorial texts focusing on information structure. Noun phrases ending with the markers -(n)un and -i/ka were annotated semi-automatically using a corpus obtained from an online newspaper. Two important factors to determine the choice of markers were examined with the annotated data: referential givenness/newness and position in a sentence. Referential givenness and newness were adopted as indicators of information structure, topic and focus respectively. In addition to quantitative analysis, qualitative analysis was conducted on the selected data. The results suggest that both the marker -(n)un and -i/ka could carry a topic and a focus reading. Sentence position also played a crucial role in determining the marker, and the marker -i/ka was used more frequently in a later position of a sentence than the marker -(n)un.

• Proceedings of the Korean Society for Information Management Conference
• /
• 2005.08a
• /
• pp.99-106
• /
• 2005

효율적이고 체계적인 정보관리를 위해 최근 연구들은 시멘틱웹(semantic web), 지식관리, 메타데이터의 통합 등에 많은 관심을 두고 있다. 그러한 연구들은 자원(resources)의 기술을 어떻게 표현할 것인가에 대한 기술구조와 그 구조를 표현하기 위한 기계 언어 등을 다루고 있다. 특히 자원의 기술을 어떻게 표현할 것인가에 대한 기술적인 구조로 가장 널리 사용되는 것은 RDF와 토픽맵(Topic Maps)을 들 수 있다. 정보조직이나 시멘틱웹 등의 연구에서 자주 등장하는 위의 개념들을 정확하게 이해하고 무엇보다 그 개념들 간의 관계를 알아보는 것이 중요할 것이다. 본 연구에서는 RDF와 토픽맵에서, 정보 즉 표현하고자 하는 대상을 표현하는 방법을 살펴보고, 두 기법간의 상호운용성에 대한 선행연구로 RDF와 토픽맵의 유사점과 차이점을 비교하고자 한다.

• Journal of Information Processing Systems
• /
• v.17 no.1
• /
• pp.163-177
• /
• 2021

Many methods of discovering social networking communities or clustering of features are based on the network structure or the content network. This paper proposes a community discovery method based on topic models using a time factor and an unsupervised clustering method. Online community discovery enables organizations and businesses to thoroughly understand the trend in users' interests in their products and services. In addition, an insight into customer experience on social networks is a tremendous competitive advantage in this era of ecommerce and Internet development. The objective of this work is to find clusters (communities) such that each cluster's nodes contain topics and individuals having similarities in the attribute space. In terms of social media analytics, the method seeks communities whose members have similar features. The method is experimented with and evaluated using a Vietnamese corpus of comments and messages collected on social networks and ecommerce sites in various sectors from 2016 to 2019. The experimental results demonstrate the effectiveness of the proposed method over other methods.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.14 no.4
• /
• pp.35-45
• /
• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.

• Journal of the Korean Society for information Management
• /
• v.22 no.3 s.57
• /
• pp.37-53
• /
• 2005

The terminology management is absolutely necessary for maintaining the efficiency of thesaurus. This is because the creating, differentiating, disappearing, and other processes of the descriptor become accomplished dynamically, making effective management of thesaurus a very difficult task. Therefore, a device is required for accomplishing methods to construct and maintain the thesaurus. This study proposes the methods to construct the thesaurus management using the basic elements of a topic map which are topic, occurrence, and association. Second, the study proposes the methods to represent the basic and specific instances using the systematic mapping algorithm and merging algorithm. Also, using a hub document as a standard, this study gives the methods to expand and subsitute the descriptors using the topic type. The new method applying fixed concept for double layer management on terms is developed, too. The purpose of this method is to fix the conceptual term which represents independent concept of time and space, and to select the descriptor freely by external information circumstance.

• Journal of Korean Society of Archives and Records Management
• /
• v.16 no.1
• /
• pp.175-193
• /
• 2016

A topic map was designed as an efficient information retrieval method that is optimized for classification, organization, and navigation through the use of a semantic link network above information resources. With this, this study aims to design a topic map-based university archives retrieval system to provide the relevant information retrieval. For this study, electronic records that relate to the academic administration within two years of D university were collected, and topic map editing was carried out with Ontopia Omnigator. Topics were classified according to their functional analysis of academic administration. In the end, the number of topics was finalized as 626, with 6 types in general: academic work, staff, college register, student, university, etc. Association was separated into six types as well, which were formed with consideration to the relationships among topics. In addition, there are seven occurrence types: register class, register number, register date, receiver, title, creator, and identifier. It is expected that the associative nature of the designed topic map-based retrieval system in this study will make navigation of large records easy and allow incidental discovery of knowledge.

• Journal of KIISE:Databases
• /
• v.32 no.6
• /
• pp.593-602
• /
• 2005

Need for machine-understandable web (Semantic web) is increasing in order for users to exactly understand Web information resources and currently there are two main approaches to solve the problem. One is the Topic map developed by the ISO/IEC JTC 1 and the other is the RDF (Resource Description Framework), one of W3C standards. Semantic web supports all of the metadata of the Web information resources, thus the necessity of interoperability between the Topic map and the RDF is required. To address this issue, several conversion methods have been proposed. However, these methods have some problems such as loss of meanings, complicated structure, unnecessary nodes, etc. In this paper, a new method is proposed to resolve some parts of those problems. The method proposed is called NOWL (NO structural Warp and semantics Loss). NOWL method gives several contributions such as maintenance of the original a Topic map instance structure and elimination of the unnecessary nodes compared with the previous researches.

• Journal of Advanced Navigation Technology
• /
• v.16 no.4
• /
• pp.673-678
• /
• 2012

Traditional culture's researcher has attempting to public use as a various method. Example is design of digital archive and digital contents. However, in spite of this effort, traditional culture's researcher has difficulty to public use. Because traditional culture is hard to understand, and less interest than the other area. Especially, traditional culture has not environment that user can searching and using the culture's information due to difficult to search the data and layers. We propose a design to make an ontology using information profile for digital contents of traditional culture. Also, we use topic-map for the factors of ontology's relation, and specify their relation using topic vector.