Text Mining Research Research Articles

Emotional Structure of Dialogues in Aging-Themed Movies: An Analysis on Text Mining

Emotions play an important role in the process of media messages transforming the audience. In our study, starting from the question of what might be the dominant emotions in old age-themed cinema texts, it is aimed to obtain clues about how old age is constructed in cinema through these emotions and what kind of emotional structure about old age is imposed on the audience. In this direction, emotion analysis was applied to the dialogues of 379 old age-themed films shot in a period of 100 years to determine what kind of emotional structure is constructed in all old age-themed cinema texts. As a result, it was found that the most dominant emotion was anger and disgust. The findings were categorized according to three different periods (1920–1969, 1970–1999, 2000–2020) and interpreted in a descriptive approach in a historical perspective and within the framework of the cinemas of the in addition to the EU, Germany, France, the UK, and Japan, Turkish cinema to make a comparison, where the most old age films were made. It is expected that our study will set an example for text mining research in cinema and offer an alternative perspective to the discussions on the phenomenon of aging in cinema.

Comparing semantic representation methods for keyword analysis in bibliometric research

Semantic representation methods play a crucial role in text mining tasks. Although numerous approaches have been proposed and compared in text mining research, the comparison of semantic representation methods specifically for publication keywords in bibliometric studies has received limited attention. This lack of practical evidence makes it challenging for researchers to select suitable methods to obtain keyword vectors for downstream bibliometric tasks, potentially hindering the achievement of optimal results. To address this gap, this study conducts an experimental comparison of various typical semantic representation methods for keywords, aiming to provide quantitative evidence for bibliometric studies. The experiment focuses on keyword clustering as the fundamental task and evaluates 22 variations of five typical methods across four scientific domains. The methods compared are co-word matrix, co-word network, word embedding, network embedding, and “semantic + structure” integration. The comparison is based on fitting the clustering results of these methods with the “evaluation standard” specific to each domain. The empirical findings demonstrate that the co-word matrix exhibits subpar performance, whereas the co-word network and word embedding techniques display satisfactory performance. Among the five network embedding algorithms, LINE and Node2Vec outperform DeepWalk, Struc2Vec, and SDNE. Remarkably, both the “pre-training and fine-tuning” model and the “semantic + structure” model yield unsatisfactory results in terms of performance. Nevertheless, even with variations in the performance of these methods, no singular approach stands out as universally superior. When selecting methods in practical applications, comprehensive consideration of factors such as corpus size and semantic cohesion of domain keywords is crucial. This study advances our understanding of semantic representation methods for keyword analysis and contributes to the advancement of bibliometric analysis by providing valuable recommendations for researchers in selecting appropriate methods.

Optimizing Clustering of Indonesian Text Data Using Particle Swarm Optimization Algorithm: A Case Study of the Quran Translation

The Quran considered the holy book for Muslims, contains scientific and historical facts affirming Islam's truth, beauty, and influence on human life. Consequently, the Quran text and its translations are valuable sources for text mining research, particularly for studying the interrelationship of its verses. One approach to grouping objects using certain algorithms is clustering, with K-Means Clustering being a prominent example. However, clustering results are often suboptimal due to the random selection of centroids. To address this, the study proposes using the Particle Swarm Optimization (PSO) algorithm, which selects centroids based on PSO results. The hybrid PSO algorithm initiates a single iteration of the K-means algorithm. It concludes either upon reaching the maximum iteration limit or when the average shift in the center of the mass vector falls below 0.0001. Evaluation of the clustering results from the three models indicates that the K-Means algorithm produced the lowest Sum of Squared Error (SSE) value of 1032.19. Additionally, the hybrid PSO algorithm generated the highest Silhouette value of 0.258 and the lowest quantization value of 0.00947. Further evaluation using a confusion matrix showed that K-Means clustering had an accuracy rate of 81.7%, K-Means with PSO had 82.5%, and the combination of K-Means with hybrid PSO yielded the highest accuracy rate of 91.1% among the three grouping model.

텍스트 마이닝을 활용한 국내 삶의 의미 연구 동향 분석

This study aims to examine the trends of research on meaning in life and provide basic data through text mining research methods. 368 academic articles related to the meaning of life were analyzed. In addition to text mining, topic modeling was used. The results of the analysis showed that, first, studies related to the meaning of life were published in 142 academic journals such as the Korean Youth Association and the Korean Counseling Association, and were studied in various fields. Second, keyword frequency analysis showed that meaning, research, result, and relationship were keywords. Third, the topic modeling analysis showed that the topics were: enhancing life satisfaction, resolving negative emotions and experiencing positive emotions, improving emotional efficacy and career adaptability, enhancing social skills, therapeutic characteristics and effects, and program development. This study is significant in that it is an attempt to present research trends related to life meaning and analyze them through text mining and topic modeling techniques, and provides limitations and suggestions along with discussion.

A Review Of TextMining Techniques: Trends, and Applications In Various Domains

Text mining, a subfield of natural language processing (NLP), has received considerable attention in recent yearsdue to its ability to extract valuable insights from large volumes of unstructured textual data. This review aims toprovide a comprehensive evaluation of the applicability of text mining techniques across various domains andindustries.The review starts off with a dialogue of the basic ideas and methodologies that are concerned with textual contentmining together with preprocessing, feature extraction, and machine learning algorithms.Furthermore, this survey highlights the challenges faced at some stage in implementing textual content miningstrategies. Additionally, the review explores emerging tendencies and possibilities in text-mining research. Itdiscusses advancements in deep learning models for text evaluation, integration with different AI technologies likeimage or speech recognition for multimodal analysis, utilization of domain-unique ontologies or information graphsfor more desirable information of textual facts, and incorporation of explainable AI strategies to improveinterpretability. The findings from this overview are analyzed to identify common developments and patterns in textmining packages across extraordinary domain names.The consequences of this paper will advantage researchers by means of imparting updated expertise of modernpractices in textual content mining. Additionally, it will manual practitioners in selecting suitable strategies for theirunique application domain names while addressing capacity-demanding situations.

BIDRN: A Method of Bidirectional Recurrent Neural Network for Sentiment Analysis

Text mining research has grown in importance in recent years due to the tremendous increase in the volume of unstructured textual data. This has resulted in immense potential as well as obstacles in the sector, which may be efficiently addressed with adequate analytical and study methods. Deep Bidirectional Recurrent Neural Networks are used in this study to analyze sentiment. The method is categorized as sentiment polarity analysis because it may generate a dataset with sentiment labels. This dataset can be used to train and evaluate sentiment analysis models capable of extracting impartial opinions. This paper describes the Sentiment Analysis-Deep Bidirectional Recurrent Neural Networks (SA-BDRNN) Scheme, which seeks to overcome the challenges and maximize the potential of text mining in the context of Big Data. The current study proposes a SA-DBRNN Scheme that attempts to give a systematic framework for sentiment analysis in the context of student input on institution choice. The purpose of this study is to compare the effectiveness of the proposed SA- DBRNN Scheme to existing frameworks to establish a robust deep neural network that might serve as an adequate classification model in the field of sentiment analysis.

GNorm2: an improved gene name recognition and normalization system.

Gene name normalization is an important yet highly complex task in biomedical text mining research, as gene names can be highly ambiguous and may refer to different genes in different species or share similar names with other bioconcepts. This poses a challenge for accurately identifying and linking gene mentions to their corresponding entries in databases such as NCBI Gene or UniProt. While there has been a body of literature on the gene normalization task, few have addressed all of these challenges or make their solutions publicly available to the scientific community. Building on the success of GNormPlus, we have created GNorm2: a more advanced tool with optimized functions and improved performance. GNorm2 integrates a range of advanced deep learning-based methods, resulting in the highest levels of accuracy and efficiency for gene recognition and normalization to date. Our tool is freely available for download. https://github.com/ncbi/GNorm2.

Research on the Performance of Text Mining and Processing in Power Grid Networks

This paper employs deep learning technique to perform the research of text mining for power grid networks, focusing on fundamental elements such as loss and activation functions. Through some analysis and formulas, we explain how these functions contribute to deep learning. We also introduce major deep learning training models, including CNN and RNN, and provide visual aids to aid understanding. To demonstrate the impact of various factors on deep learning training, we employ control variable experiments to analyze the influence of factors such as learning rate, batch size, and data noise on model training trends. While the influence of hyperparameters and data noise are covered in this paper, other factors such as CPU and memory frequency, as well as GPU performance, also play a crucial role in deep learning training. Therefore, continuous adjustments to various factors are necessary to achieve optimal training results for deep learning models in power grid networks.

Does political affiliation impact transport policy debate?: Evidences from social media

AbstractSocial networking sites (SNS) have played an important role in engaging citizens to various policy discussions and debates about government, society and environment. Though SNS platforms empower citizens to express their opinion on important topics, several concerns have been raised by researchers for using SNS as discussion platform for important political, government and environmental policies. SNS platforms may be manipulated by the dominant group and citizen might also display group behaviour on these platforms while expressing their opinions of digital platforms. This study analyses political affiliations’ influence on opinion and content shared by various stakeholders on Twitter. The context of odd‐even driving policy is undertaken using text mining and statistical research methods. This research suggests that political affiliation significantly affects type and characteristics of content shared by SNS users. This study will help policymakers and researchers in better understanding of citizens responses to environmental policies on SNS platforms.

Supply chain strategy during the COVID-19 terms: sentiment analysis and knowledge discovery through text mining

The coronavirus pandemic has affected not only health but also the economy. The use of big data in finding information can be used to gain profits that logistics companies can utilize to survive during the pandemic. This study conducted text-mining research on service consultant sites in the logistics sector. This study aims to present frequency diagrams, analyze sentiment using the National Research Council (NRC) lexicon, present bigrams, and seek knowledge about strategies to minimize shipping costs and maintain inventories of manufactured goods. The words "supply", "chain", and "COVID-19" are words that are used frequently throughout the article. The results of this study showed that the words that often appear from word excavation are the words "supply", "chain", "logistics", "kpis," and "inventory". Then emotion trust becomes an emotional word that often appears in articles. The words "Supply" and "pandemic" are the words that seem the most positive and negative words, respectively. The words "COVID-19", "safety stock", and "inventory management" are words that often appear together. The result of discovery knowledge is that logistics consultants offer emotions of trust and provide many insights on minimizing shipping costs and maintaining inventory during a pandemic.

OpCitance: Citation contexts identified from the PubMed Central open access articles

OpCitance contains all the sentences from 2 million PubMed Central open-access (PMCOA) articles, with 137 million inline citations annotated (i.e., the “citation contexts”). Parsing out the references and citation contexts from the PMCOA XML files was non-trivial due to the diversity of referencing style. Only 0.5% citation contexts remain unidentified due to technical or human issues, e.g., references unmentioned by the authors in the text or improper XML nesting, which is more common among older articles (pre-2000). PubMed IDs (PMIDs) linked to inline citations in the XML files compared to citations harvested using the NCBI E-Utilities differed for 70.96% of the articles. Using an in-house citation matcher, called Patci, 6.84% of the referenced PMIDs were supplemented and corrected. OpCitance includes fewer total number of articles than the Semantic Scholar Open Research Corpus, but OpCitance has 160 thousand unique articles, a higher inline citation identification rate, and a more accurate reference mapping to PMIDs. We hope that OpCitance will facilitate citation context studies in particular and benefit text-mining research more broadly.

Unlocking the potential of public opinions for niche development using acceptance mining

This paper proposes a combination of text mining and acceptance research as a methodological approach (“acceptance mining”) to facilitate the development of innovative technologies in niches. At the example of recharging infrastructures for electric vehicles, the approach is explored on a sample of over 20,000 user comments collected from social media and discussion forums. The results indicate that acceptance mining provides valuable insights into the demands of the public during all phases of niche development which allow shaping niches in socially acceptable ways. In particular, the method reveals requirements and risks of niche-development measures such as shielding, nurturing, and empowering niches as perceived by the public. The results are discussed regarding the potentials of acceptance mining for developing innovations in niches as well as its limitations and possibilities for optimization. Acceptance mining and its application to a case study are especially of interest for researchers and practitioners involved in niche development; the study results provide valuable insights for professionals engaged in developing recharging infrastructures for electric vehicles.

Identification of relevant features influencing movie reviews using sentiment analysis

Sentiment analysis is a systematic text mining research that examines individuals' behaviour, approach, and viewpoint. This paper analyses viewers' sentiments towards the movies released during the pandemic. This study employs the sentiment analysis techniques on movie reviews' accessed in real-time from internet movie database (IMDb). The paper's main objective is to identify the potential words that contribute to the biases of the reviews and influence overall viewers. The proposed methodology has employed valence aware dictionary for sentiment reasoning based on sentiment analysis of overall reviews, followed by application to various movie genres. Finally, we have applied Pearson's correlation analysis to find the association between the words among the genres. The paper also calculates the sentiment scores of reviews using different sentiment analysis models. Our results showed a minimum of 17% features common genre-wise. It reveals sets of most distinct influential words, which may be vital for understanding the nature of the language used for a particular kind of movie.

Text classification technique for discovering country-based publications from international COVID-19 publications.

The significant increase in the number of COVID-19 publications, on the one hand, and the strategic importance of this subject area for research and treatment systems in the health field, on the other hand, reveals the need for text-mining research more than ever. The main objective of the present paper is to discover country-based publications from international COVID-19 publications with text classification techniques. The present paper is applied research that has been performed using text-mining techniques such as clustering and text classification. The statistical population is all COVID-19 publications from PubMed Central® (PMC), extracted from November 2019 to June 2021. Latent Dirichlet allocation (LDA) was used for clustering, and support vector machine (SVM), scikit-learn library, and Python programming language were used for text classification. Text classification was applied to discover the consistency of Iranian and international topics. The findings showed that seven topics were extracted using the LDA algorithm for international and Iranian publications on COVID-19. Moreover, the COVID-19 publications show the largest share in the subject area of "Social and Technology in COVID-19" at the international (April 2021) and national (February 2021) levels with 50.61% and 39.44%, respectively. The highest rate of publications at international and national levels was in April 2021 and February 2021, respectively. One of the most important results of this study was discovering a common trend and consistency of Iranian and international publications on COVID-19. Accordingly, in the topic category "Covid-19 Proteins: Vaccine and Antibody Response," Iranian publications have a common publishing and research trend with international ones.

Legal reform to enhance global text and data mining research.

Outdated copyright laws around the world hinder research.

A bibliometric review of research on interorganizational conflicts in the construction industry: 1989-2021

PurposeThe issue of conflict has been around for a long time. With the development of economic development and the diversification of industries, conflicts have shown a trend of complex development. By reviewing interorganizational conflict in construction, this study aims to identify the formation, development and evolution of conflict and to assist organizational managers in using scientific conflict management tools to deal with and resolve conflict.Design/methodology/approachThis paper considers the following questions: “What is the overall research status of the interorganizational conflict in construction projects?” and “What are the trendy issues in interorganizational conflicts in construction projects?” This study uses R software's Biblioshiny program to describe the overall conflict between organizations to answer question 1. This study uses Netminer – a social network analysis (SNA) software and text mining to explore research trends and answer question 2.FindingsInterorganizational conflict has been one of the most important topics in recent years when it comes to conflict issues. This paper intends to examine interorganizational conflict in construction in an attempt to shed light on four main topics regarding the conflict: the application of conflict theory; factors that induce conflict; the effect of conflict on performance; and methods of conflict management.Research limitations/implicationsThrough a comprehensive analysis of interorganizational conflict, this study helps to increase the understanding of scholars in the field of conflict research. This study suggests future research directions and provides references for managers to improve their conflict management, decision-making and performance.Originality/valueThis study combines bibliometrics, SNA and text mining research methods. Multidimensional and multiperspectival assessments are performed based on the status of the research, its evolution and trends. The results of this study can be used to guide the resolution of conflict in other industries. This study also enriches the scientific theory related to the interorganizational conflict.

CLASSIFICATION OF LARGE DOCUMENTS USING MACHINE LEARNING TECHNIQUES

Document classification is a growing interest in the research of text mining. Correctly identifying the documents into particular category is still presenting challenge because of large and vast amount of features in the dataset. In regards to the existing classifying approaches, Naive Bayes is potentially good at serving as a document classification model due to its simplicity. The aim of this paper is to highlight the performance of employing Naive Bayes in document classification. Results show that Naive Bayes is the best classifiers against several common classifiers (such as decision tree, neural network, and support vector machines) in term of accuracy and computational efficiency.

BertSRC: transformer-based semantic relation classification

The relationship between biomedical entities is complex, and many of them have not yet been identified. For many biomedical research areas including drug discovery, it is of paramount importance to identify the relationships that have already been established through a comprehensive literature survey. However, manually searching through literature is difficult as the amount of biomedical publications continues to increase. Therefore, the relation classification task, which automatically mines meaningful relations from the literature, is spotlighted in the field of biomedical text mining. By applying relation classification techniques to the accumulated biomedical literature, existing semantic relations between biomedical entities that can help to infer previously unknown relationships are efficiently grasped. To develop semantic relation classification models, which is a type of supervised machine learning, it is essential to construct a training dataset that is manually annotated by biomedical experts with semantic relations among biomedical entities. Any advanced model must be trained on a dataset with reliable quality and meaningful scale to be deployed in the real world and can assist biologists in their research. In addition, as the number of such public datasets increases, the performance of machine learning algorithms can be accurately revealed and compared by using those datasets as a benchmark for model development and improvement. In this paper, we aim to build such a dataset. Along with that, to validate the usability of the dataset as training data for relation classification models and to improve the performance of the relation extraction task, we built a relation classification model based on Bidirectional Encoder Representations from Transformers (BERT) trained on our dataset, applying our newly proposed fine-tuning methodology. In experiments comparing performance among several models based on different deep learning algorithms, our model with the proposed fine-tuning methodology showed the best performance. The experimental results show that the constructed training dataset is an important information resource for the development and evaluation of semantic relation extraction models. Furthermore, relation extraction performance can be improved by integrating our proposed fine-tuning methodology. Therefore, this can lead to the promotion of future text mining research in the biomedical field.

A Survey for News Credibility in Social Networks

Text mining has been a vital area that has been linked to some fields of research such as machine learning, data analysis and gathering, and information recovery. To extract knowledge and information, Natural Language Processing (NLP) was used alternative techniques. Text mining analyses unstructured data to provide critical data and information plans in a timely manner. Nowadays everyone uses online communication activities to keep in touch with others in their daily life. As a result, they're a great way to connect. Not sorting in a paragraph in a format suitable for word recognition has become a point of contention. intensity can cause a variety of inconsistencies, such as lexical, semantic, linguistic, and syntactic ambiguities, determining the proper data arrangement. Information and data are required for learning things and reaching knowledge. This paper covered how to use text mining to determine the credibility of news on social media. The findings of this study could be used as the basis for future text mining research.

TOPIC MODELING BY USING LDA

Topic modelling plays a significant Information retrieval showed good performance on a wide variety of tasks over the years. It has wide research in machine learning and text mining. The combination of the probability mixture model and the term model presents the topic model. They are the language modelling framework of information retrieval. The present topic modelling methods are probabilistic latent semantic analysis and latent dirichlet allocation. Two main faults of topic modelling are First, common or popular words are different topics, often causing ambivalence to understand the topic. Second, single words have to be presented correctly. Actual problems of topic modelling are Efficiency and scaling are discussed and compared in different types of Topic modelling. The review of article is about how topic modelling for information retrieval is done using LDA(Latent Dirichlet Allocation).