• Title/Summary/Keyword: Text mining analysis

Search Result 1,187, Processing Time 0.022 seconds

A Study on De-Identification Methods to Create a Basis for Safety Report Text Mining Analysis (항공안전 보고 데이터 텍스트 분석 기반 조성을 위한 비식별 처리 기술 적용 연구)

  • Hwang, Do-bin;Kim, Young-gon;Sim, Yeong-min
    • Journal of the Korean Society for Aviation and Aeronautics
    • /
    • v.29 no.4
    • /
    • pp.160-165
    • /
    • 2021
  • In order to identify and analyze potential aviation safety hazards, analysis of aviation safety report data must be preceded. Therefore, in consideration of the provisions of the Aviation Safety Act and the recommendations of ICAO Doc 9859 SMM Edition 4th, personal information in the reporting data and sensitive information of the reporter, etc. It identifies the scope of de-identification targets and suggests a method for applying de-identification processing technology to personal and sensitive information including unstructured text data.

Analysis of Real Estate Market Trend Using Text Mining and Big Data (빅데이터와 텍스트마이닝을 이용한 부동산시장 동향분석)

  • Chun, Hae-Jung
    • Journal of Digital Convergence
    • /
    • v.17 no.4
    • /
    • pp.49-55
    • /
    • 2019
  • This study is on the trend of real estate market using text mining and big data. The data were collected through internet news posted on Naver from August 2016 to August 2017. As a result of TF-IDF analysis, the frequency was high in the order of housing, sale, household, real estate market, and region. Many words related to policies such as loan, government, countermeasures, and regulations were extracted, and the region - related words appeared the most frequently in Seoul. The combination of the words related to the region showed that the frequencies of 'Seoul - Gangnam', 'Seoul - Metropolitan area', 'Gangnam - reconstruction' and 'Seoul - reconstruction' appeared frequently. It can be seen that the people's interest and expectation about the reconstruction of Gangnam area is high.

Customer Value Proposition Methodology Using Text Mining of Online Customer Reviews (온라인 고객 리뷰에 대한 텍스트마이닝을 활용한 고객가치제안 방법)

  • Han, Young-Kyung;Kim, Chul-Min;Park, Kwang-Ho
    • Journal of Korean Society of Industrial and Systems Engineering
    • /
    • v.44 no.4
    • /
    • pp.85-97
    • /
    • 2021
  • Online consumer activities have increased considerably since the COVID-19 outbreak. For the products and services which have an impact on everyday life, online reviews and recommendations can play a significant role in consumer decision-making processes. Thus, to better serve their customers, online firms are required to build online-centric marketing strategies. Especially, it is essential to define core value of customers based on the online customer reviews and to propose these values to their customers. This study discovers specific perceived values of customers in regard to a certain product and service, using online customer reviews and proposes a customer value proposition methodology which enables online firms to develop more effective marketing strategies. In order to discover customers value, the methodology employs a text-mining technology, which combines a sentiment analysis and topic modeling. By the methodology, customer emotions and value factors can be more clearly defined. It is expected that online firms can better identify value elements of their respective customers, provide appropriate value propositions, and thus gain sustainable competitive advantage.

Analysis of speech in game marketing video using text mining techniques (텍스트 마이닝 기법을 이용한 게임 마케팅 비디오에서의 스피치 분석)

  • Lee, Yeokyung;Kim, Jaejik
    • The Korean Journal of Applied Statistics
    • /
    • v.35 no.1
    • /
    • pp.147-159
    • /
    • 2022
  • Nowadays, various social media platforms are widely spread and people closely use such platforms in daily life. By doing so, social influencers with a large number of subscribers, views, and comments have huge impact in our society. Following this trend, many companies are actively using influencers for marketing purpose to promote their products and services. In this study, we extract the speeches of influencers from videos for game marketing and analyze them using various text mining techniques. In the analysis, we distinguish game videos leading to successful marketing and failed marketing, and we explore and compare the linguistic features of the influencers for successful and failed marketings.

Analysis of VR Game Trends using Text Mining and Word Cloud -Focusing on STEAM review data- (텍스트마이닝과 워드 클라우드를 활용한 VR 게임 트렌드 분석 -스팀(steam) 리뷰 데이터를 중심으로-)

  • Na, Ji Young
    • Journal of Korea Game Society
    • /
    • v.22 no.1
    • /
    • pp.87-98
    • /
    • 2022
  • With the development of fourth industrial revolution-related technology and increased demands for non-face-to-face services, VR games attract attention. This study collected VR game review data from an online game platform STEAM and analyzed chronical trends using text mining and word cloud analysis. According to the results, experience and perceived cost were major trends from 2016 to 2017, increased demands for FPS and rhythm games were from 2018 to 2019, and story and immersion were from 2020 to 2021. It aims to contribute to expanding the base of VR games by identifying the keywords VR users take interest in by period.

Analysis of Consumer Value Structure in Vintage Clothing Consumption -Based on Text Mining and Means-End Chain Analysis- (빈티지 의류 소비에서의 소비자 가치구조 분석 -텍스트 마이닝 기법과 수단-목적 사슬 분석을 중심으로-)

  • Yujeong Won;Chanhee Kang;Yuri Lee
    • Journal of the Korean Society of Clothing and Textiles
    • /
    • v.47 no.4
    • /
    • pp.729-742
    • /
    • 2023
  • This two-part study explores the changes in the types of perceived value and consumption channels for vintage clothing and the relationship between the two variables. In Study 1, we used text mining with the keyword "fashion+vintage." Emotional value was the most frequently mentioned, and environmental value increased the most. We also revealed an increasing trend in online channels for vintage clothing consumption. In Study 2, we analyzed 30 interviews with consumers who had purchased vintage clothing through online channels. We identified 7 attributes and 20 goals for vintage consumption online and pinpointed three strong connections. First, consumers reported high levels of service satisfaction due to the usefulness of algorithms. Second, the authenticity and heritage information available through online and mobile channels were associated with consumers' perceptions of value related to financial benefits. Third, consumers sought to find rare products through online channels, leading to a strong influence on their sense of achievement. Overall, this study proposed ways to increase the value of vintage clothing perceived by consumers through consumption online.

The Ebb and Flow of Regional Integration Vision in Asia-Pacific: From a Lens of Leaders' Declarations over 30 Years

  • Jeongmeen Suh
    • East Asian Economic Review
    • /
    • v.27 no.4
    • /
    • pp.303-325
    • /
    • 2023
  • This paper examines how APEC has transformed itself into an international forum for the vision of regional integration. It aims to quantify the documentation produced by the international organization and provide quantifiable evidence that aligns with prior knowledge rather than relying solely on intuition. For this purpose, I use various text mining techniques to extract multi-dimensional features from the text of APEC Leaders' Declarations from 1993 to 2023. In terms of interest and expectations for APEC as a forum, it is found that members have experienced two major peaks and troughs over the last three decades. It is found that the change point coincides with the Asian financial crisis of 1997 and the tensions between the United States and China since 2017. To explore more various aspects of economic integration in the Asia-Pacific region, this study also considers how consistently APEC has been an international forum for addressing issues, which members are active, and how members have clustered based on their views of APEC.

Predicting numeric ratings for Google apps using text features and ensemble learning

  • Umer, Muhammad;Ashraf, Imran;Mehmood, Arif;Ullah, Saleem;Choi, Gyu Sang
    • ETRI Journal
    • /
    • v.43 no.1
    • /
    • pp.95-108
    • /
    • 2021
  • Application (app) ratings are feedback provided voluntarily by users and serve as important evaluation criteria for apps. However, these ratings can often be biased owing to insufficient or missing votes. Additionally, significant differences have been observed between numeric ratings and user reviews. This study aims to predict the numeric ratings of Google apps using machine learning classifiers. It exploits numeric app ratings provided by users as training data and returns authentic mobile app ratings by analyzing user reviews. An ensemble learning model is proposed for this purpose that considers term frequency/inverse document frequency (TF/IDF) features. Three TF/IDF features, including unigrams, bigrams, and trigrams, were used. The dataset was scraped from the Google Play store, extracting data from 14 different app categories. Biased and unbiased user ratings were discriminated using TextBlob analysis to formulate the ground truth, from which the classifier prediction accuracy was then evaluated. The results demonstrate the high potential for machine learning-based classifiers to predict authentic numeric ratings based on actual user reviews.

Safety Culture: A Retrospective Analysis of Occupational Health and Safety Mining Reports

  • Tetzlaff, Emily J.;Goggins, Katie A.;Pegoraro, Ann L.;Dorman, Sandra C.;Pakalnis, Vic;Eger, Tammy R.
    • Safety and Health at Work
    • /
    • v.12 no.2
    • /
    • pp.201-208
    • /
    • 2021
  • Background: In the mining industry, various methods of accident analysis have utilized official accident investigations to try and establish broader causation mechanisms. An emerging area of interest is identifying the extent to which cultural influences, such as safety culture, are acting as drivers in the reoccurrence of accidents. Thus, the overall objective of this study was to analyze occupational health and safety (OHS) reports in mining to investigate if/how safety culture has historically been framed in the mining industry, as it relates to accident causation. Methods: Using a computer-assisted qualitative data analysis software, 34 definitions of safety culture were analyzed to highlight key terms. Based on word count and contextual relevance, 26 key terms were captured. Ten OHS reports were then analyzed via an inductive thematic analysis, using the key terms. This analysis provided a concept map representing the 50-year data set and facilitated the use of text framing to highlight safety culture in the selected OHS mining reports. Results: Overall, 954 references and six themes, safety culture, attitude, competence, belief, patterns, and norms, were identified in the data set. Of the 26 key terms originally identified, 24 of them were captured within the text. The results made evident two distinct frames in which to interpret the data: the role of the individual and the role of the organization, in safety culture. Conclusion: Unless efforts are made to understand and alter cultural drivers and share these findings within and across industries, the same accidents are likely to continue to occur.

A Study on the Finding of Promising Export Items in Defense industry for Export Market Expansion-Focusing on Text Mining Analysis-

  • Yeo, Seoyoon;Jeong, Jong Hee;Kim, Seong Ho
    • Journal of the Korea Society of Computer and Information
    • /
    • v.27 no.10
    • /
    • pp.235-243
    • /
    • 2022
  • This paper aims to find promising export items for market expansion of defense export items. Germany, the UK, and France were selected as export target countries to obtain unstructured forecast data on weapons system acquisition plans for the next ten years by each country. Using the TF-IDF in text mining analysis, keywords that appeared frequently in data from three countries were derived. As a result of this paper, keywords for each country's major acquisition projects drawing. However, most of the derived keywords were related to mainstay weapon systems produced by domestic defense companies in each country. To discover promising export items from text mining, we proposed that the drawn keywords are distinguished as similar weapon systems. In addition, we assort the weapon systems that the three countries will get a plan to acquire commonly. As a result of this paper, it can be seen that the current promising export item is a weapon system related to the information system. Prioritizing overseas demands using key words can set clear market entry goals. In the case of domestic companies based on needs, it is possible to establish a specific entry strategy. Relevant organizations also can provide customized marketing support.