Topic LDA Research Articles

유튜브 댓글 분석을 통한 클래식 음악 시청자 반응 연구

This study analyzed Korean-language comments on YouTube videos of pianist Lim Yunchan’s performance of Rachmaninoff’s Piano Concerto No. 3 during the 2022 Van Cliburn International Piano Competition finals, exploring domestic viewers’ reactions and emotional perceptions of classical music. The research methodology involved selecting only Korean comments collected via the YouTube Data API from June 20, 2022, to December 1, 2023, refining the data, and conducting keyword frequency analysis, LDA Topic Modeling, and sentiment analysis using R. The analysis indicated that viewers expressed deep emotional resonance and admiration for the artistic excellence of Lim’s performance through the identified keywords. The LDA Topic Modeling yielded three themes: ‘the Van Cliburn Competition’, ‘viewer responses’, and ‘the bright future of K-Classic’. Although the sentiment analysis showed some limitations, predominantly positive responses were observed. This study provides substantive data on how viewing classical music performances on YouTube can elicit emotional resonance and admiration for artistic excellence in viewers. Additionally, it confirmed that YouTube plays a crucial role in enhancing the accessibility of classical music and serving as a conduit for appreciation and communication. This research contributes to understanding the current landscape of Korea’s classical music consumption and offers insights into the consumption patterns of classical music in the digital media era.

텍스트 마이닝을 통한 문화도시의 문화관광 콘텐츠 요인 연구

Purpose: The purpose of this study was to examine the factors affecting cultural tourism content in cultural cities and to broaden our understanding of the relationship between cultural cities and cultural tourism.
 Methods: This study conducted text mining on social media big data using 'Seogwipo Cultural City' as a search term. TF-IDF (Term Frequency - Inverse Document Frequency) analysis, Centrality Analysis, Structural Equivalence Analysis, and LDA Topic Modelling were used to analyze the social media data.
 Results: As a result of the analysis, it was confirmed that it is possible to distinguish between culture cluster keywords such as ‘noji culture’ and ‘bookstore day’ through Structural Equivalence Analysis and tourism cluster keywords such as ‘travel’, ‘tourism’, ‘course’, and ‘space’. In addition, through topic modelling, topics related to cultural tourism such as 'bookstore culture contents' and 'Seogwipo Cultural City Art Festival' were classified, and cultural tourism contents were derived.
 Conclusion: The derivation of cultural tourism content through text mining was a method of exploring cultural tourism content factors after the establishment of a cultural city, which was different from the existing method of considering tourism from the stage of promoting a cultural city. This reactive exploration of cultural tourism content, which derived themes for cultural tourism based on cultural cities, could suggest the possibility of coexistence of cultural tourism with the establishment of local cultural ecosystems in cultural cities. In addition, this approach was significant in that it could dispel concerns about various social problems arising from the touristification of cities.

ETNT: Enhanced TextNetTopics with Filtered LDA Topics and Sequential Forward / Backward Topic Scoring Approaches

eTNT: Enhanced TextNetTopics with Filtered LDA Topics and Sequential Forward / Backward Topic Scoring Approaches

Malaysia Citizen Sentiment on Government Response Towards Covid-19 Disaster Management: Using LDA-based Topic Visualization on Twitter

This paper studies lessons learned from Covid-19 disaster management in Malaysia using machine learning techniques. First, we crawl Twitter data related to ‘covid’ with geo-location bounding-box. Then we contribute to propose LDA topics generated on citizen perception containing negative sentiment towards government response; hence, we represent the data using VOSviewer and D3.js to emphasize topic modeling with respect to timestamp due to pattern analysis. As results, LDA-based topic visualization may recognize the accounts’ pattern that are assumed as the pillars of disaster management in Malaysia. This study gains insights from political science field. Implications of the results are also discussed.

LDA Topic Analysis for Product Reviews in Social Media Platform

Social media in recent years is used as platform for product reviews and customer feedback. Thus, to understand the topic which have been discussed, we utilized Latent Dirichlet Allocation for topic modeling. The topic modeling is important due to it can gain insights into the specific features that customers like or dislike about a particular product. Thus, by not using stop words due it have possibilities remove the time domain, the information can be valuable for businesses as it helps them understand customer preferences and inform product development and marketing strategies with the coherence score 0.621520, the topic modeling obtained 3 optimal topics, where the topic 0 discussed about price and time it will be available to purchase. In topic 1 it discussed about the product is hard to obtain due to it not available in market. In topic 2, it discussed about ownership (what they like after usage).

Data Analysis of Psychological Approaches to Soccer Research: Using LDA Topic Modeling.

This study identifies the topical areas of research that have attempted a psychological approach to soccer research over the last 33 years (1990-2022) and explored the growth and stagnation of the topic as well as research contributions to soccer development. Data were obtained from 1863 papers from the Web of Science database. The data were collected through keyword text mining and data preprocessing to determine the keywords needed for analysis. Based on the keywords, latent Dirichlet allocation-based topic modeling analysis was performed to analyze the topic distribution of papers and explore research trends by topic area. The topic modeling process included four topic area and fifty topics. The "Coaching Essentials in Football" topic area had the highest frequency, but it was not statistically identified as a trend. However, coaching, including training, is expected to continue to be an important research topic, as it is a key requirement for success in the highly competitive elite football world. Interest in the research field of "Psychological Skills for Performance Development" has waned in recent years. This may be due to the predominance of other subject areas rather than a lack of interest. Various high-tech interventions and problem-solving attempts are being made in this field, providing opportunities for qualitative and quantitative expansion. "Motivation, cognition, and emotion" is a largely underrated subject area in soccer psychology. This could be because survey-based psychological evaluation attempts have decreased as the importance of rapid field application has been emphasized in recent soccer-related studies. However, measuring psychological factors contributes to the study of football psychology through a new methodology and theoretical background. Recognizing the important role of psychological factors in player performance and mental management, as well as presenting new research directions and approaches that can be directly applied to the field, will advance soccer psychology research.

LDA 토픽 모델링을 활용한 무선이어폰 소비자 구매 의도 및 만족도 비교 분석 : 쿠팡에서의 삼성과 애플 무선이어폰 리뷰를 중심으로

LDA 토픽 모델링을 활용한 무선이어폰 소비자 구매 의도 및 만족도 비교 분석 : 쿠팡에서의 삼성과 애플 무선이어폰 리뷰를 중심으로

Research Trends in Liberal Arts Studies Before and After the COVID-19 Pandemic Using LDA Topic Modeling

This study analyzes the changing trends of research topics in liberal arts studies before and after the COVID-19 pandemic. It aims to investigate the impact of the pandemic on research trends in liberal arts studies, identify changes and new trends in recent years with the pandemic, and contribute to the search for the future direction of liberal arts studies in the post-COVID-19 era. To achieve these objectives, we applied keyword frequency analysis and LDA topic modeling analysis as text mining technologies to analyze the keywords and topics of research papers in liberal arts education before and after the pandemic. Our findings revealed meaningful differences in the topics and keywords of papers in liberal arts education studies before and after the COVID-19 pandemic. We interpret these results as reflecting the changes and directions of liberal arts education in the actual field. We also analyzed individual papers on extracted topics and keywords to more elaborately interpret the results and the meaning of the analyses. The results showed that new topics, such as COVID-19 and untact, emerged after the pandemic. The frequency of topics related to online education also increased after the pandemic. We also found an expansion of convergence and integration related to research topics in liberal arts education, as well as a considerable increase of interest in topics regarding artificial intelligence, literacy, and PBL after the pandemic. Based on the interests of researchers and trends in liberal arts education research, this study contributes to ongoing efforts to learn more about the impact of COVID-19 on the actual educational practices of liberal arts education. It is also significant in that it enables the observation and prediction of changes and trends in the research and actual fields of liberal arts education in the future.

LDA Topic Modeling on the Trends of the Next Digital Generation on the New Internet Revolution, Metaverse

The purpose of this study is to explore the trends of the Z and Alpha generation user groups on the metaverse for each unique topic by using LDA topic modeling based on big data analysis. This study seeks a new sustainability direction for the metaverse by discovering the internal and experiential value of users' experiences beyond the technical perspective of the metaverse. The worldwide enthusiasm for the metaverse was driven by the impact of the COVID-19 pandemic, technological advances in computer graphics and infrastructure, and the expansion of the digital generation user base such as generation Z and Alpha. Research on the metaverse has been increasing since 2021, but theoretical approaches by generational subgroups of nextgeneration digital users on the metaverse are still insufficient. Considering the timeliness of the research topic, this study subdivides the next-generation users into two groups, the Z generation and the Alpha generation, to discover the characteristics of each generation regarding the metaverse. In the process of analysis, the importance and relevance of words were identified by text mining analysis of unstructured big data. Next, through LDA topic modeling and visualization analysis, the meaning of each topic group was interpreted based on word pockets, which are related words that have mutually exclusive uniqueness for each topic. Python 3.10 and Textom 6 version software were used for analysis. This study will present meaningful academic and practical insights into the sustainability and utilization of the metaverse by a diverse user base. Keywords—Big data, Data mining, LDA Topic Modeling, Metaverse, Digital Generation

Sentiment Analysis of Yaya Event of Giant Panda in the United States based on LDA Topic Model

With the rapid rise of the Internet and social media, hot topics can often become hotspots of online public opinion. In order to explore the netizens’ views on hot topics, this paper constructs an LDA model to analyse the sentiment of the incident of travelling panda Yaya by crawling the Weibo comments on the incident of travelling Giant Panda Yaya in the U.S. The study shows that netizens suspect that Yaya has suffered some abuse in the U.S. Memphis Zoo, and urgently hope that the panda Yaya will come back to China as soon as possible to receive treatment and care in the domestic zoo. The results of the study are helpful for the relevant departments to understand the attitudes and tendencies of netizens towards hot topics in a timely manner, and quickly deduce the development trend of the topics, so as to guide the public opinion accordingly.

Topic Discovery in Operations Management Research Area Based on LDA Topic Modeling

Topic Discovery in Operations Management Research Area Based on LDA Topic Modeling

Tourism Review Text Mining in Guizhou Province Based on LDA Topic Model

The rapid development of the Internet has brought more convenience to people's lives. Consumers begin to choose to obtain services through the Internet, and tourism has gradually become an important member of e-commerce. Most tourists choose to learn about scenic spots online in advance and buy tickets through tourism websites. In the face of a large number of consumers, scenic spot managers should pay attention to online feedback information so as to improve all aspects of the scenic spot. In this paper, the characteristic tourist attractions of Guizhou are taken as an example, the octopus collector is used to climb the comment information of major tourism websites, and the comment information is processed by python and R, the word cloud map of positive and negative emotion words is constructed, and the feature words with high attention are obtained. The topic extraction is carried out by the LDA model, and the comment information is further analyzed by combining the positive and negative emotion words. The paper focuses on exploring the concerns of tourists, and puts forward suggestions with commercial value for Guizhou characteristic tourist attractions.

Metre and Semantics in the Poetry of Czech Post-Symbolists Accessed via LDA Topic Modelling

The article deals with the relationship between semantics and poetic meter in the works of Czech post-symbolist poets and their predecessors. We access the phenomena by means of a machine-driven meter recognition on one hand and LDA topic modelling on the other. We first show how the poetic groups differ in their general preferences for particular topics. Next we analyze the topic distributions in two dominant metres (i.e. iamb and trochee) across the poetic groups.

A Novel Approach for Discovering the Patterns by using PDBD Model in Big Data

Big data is a term that refers to information collected from a wide range of sources, including transaction processing systems, sensors, digital photographs, internet click stream logs, movies, and social media. The goal of text mining is to reveal new patterns and relationships that could lead to the discovery of previously unknown sources. It provides ample scope for interpretation of genuine user intent or contextual meanings as the case be. This research focuses on the creation of a novel approach for discovering patterns in big data using the Pattern Discovery in Big Data (PDBD) model in text mining. From textual sources, text-mining techniques extract a range of patterns and important facts. Sequential pattern mining helps to extract informative elements from a set of sequences based on the frequency of occurrences. The Pattern Discovery in Big Data (PDBD) model study has four primary contributions to uncovering patterns uniquely and effectively: Semantic information extraction, pattern improvements, LDA Topic Modeling, and cluster assignments. The proposed work compared the Pattern Taxonomy Model (Inner Pattern Evolving) pattern inner evolving model with other existing similar benchmark models to evaluate the model performance. The empirical results prove that the proposed model outperformed the compared models with significant improvement in accuracy Improvement in this Pattern Discovery in Big Data (PDBD) model yields better results in discovering the patterns.

Selection of the Optimal Number of Topics for LDA Topic Model-Taking Patent Policy Analysis as an Example.

This study constructs a comprehensive index to effectively judge the optimal number of topics in the LDA topic model. Based on the requirements for selecting the number of topics, a comprehensive judgment index of perplexity, isolation, stability, and coincidence is constructed to select the number of topics. This method provides four advantages to selecting the optimal number of topics: (1) good predictive ability, (2) high isolation between topics, (3) no duplicate topics, and (4) repeatability. First, we use three general datasets to compare our proposed method with existing methods, and the results show that the optimal topic number selection method has better selection results. Then, we collected the patent policies of various provinces and cities in China (excluding Hong Kong, Macao, and Taiwan) as datasets. By using the optimal topic number selection method proposed in this study, we can classify patent policies well.

Characterizing Geographic Variation in Well-Being Using Tweets

The language used in tweets from 1,300 different US counties was found to be predictive of the subjective well-being of people living in those counties as measured by representative surveys. Topics, sets of co-occurring words derived from the tweets using LDA, improved accuracy in predicting life satisfaction over and above standard demographic and socio-economic controls (age, gender, ethnicity, income, and education). The LDA topics provide a greater behavioural and conceptual resolution into life satisfaction than the broad socio-economic and demographic variables. For example, tied in with the psychological literature, words relating to outdoor activities, spiritual meaning, exercise, and good jobs correlate with increased life satisfaction, while words signifying disengagement like ’bored’ and ’tired’ show a negative association.

Finding topic boundaries in literary text

When performing a distant reading analysis of large amounts of literary texts, we would like to be able to automatically identify the high level structure or story lines of these texts. Story lines are not always linear, but contain transitions, such as flashbacks or changes of scenery. To identify these transitions, we propose a system that aims to identify a boundary describing such a transition. First, we split the text in short snippets. Next, topics are assigned to each of the snippets using LDA, a topic modelling approach. Based on this sequence of LDA topics, potential transition boundaries between snippets are identified. Potential transitions occur between snippets with the smallest intersection of the LDA topics that occur on either side of the potential transition. If multiple potential transitions are available, the system selects one at random. To evaluate this system, we apply it to the concatenation of two texts such that the real boundary is known. We provide results of this system with respect to a random baseline and an oracle system that always selects the best transition when more than one possible transition is available. The system consistently outperforms the baseline. Future work will focus on extending this system to allow for the identification of multiple transitions.

The Arms Race on the Road: Exploring Factors of SUVs’ Popularity by LDA Topic Model

The Arms Race on the Road: Exploring Factors of SUVs’ Popularity by LDA Topic Model

Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling.

The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time. As government policy and health measures shift, public perceptions and concerns also change, an evolution documented within discourse on social media. We propose a dynamic content-specific LDA topic modeling technique that can help to identify different domains of COVID-specific discourse that can be used to track societal shifts in concerns or views. Our experiments show that these model-derived topics are more coherent than standard LDA topics, and also provide new features that are more helpful in prediction of COVID-19 related outcomes including mobility and unemployment rate.

Short text semantic feature extension and classification based on LDA

To solve the problem of feature sparseness of short texts, we studied the application of LDA Topic Model on feature extension and classification of short texts. Training LDA on external long texts related to short texts, and achieving the inference and extension of short texts’ topics based on LDA solves the feature sparseness of short texts and improves the accuracy of classification effectively. Latent semantic information in LDA can also effectively improve the interpretability of short texts.