Text Mining

Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.

In recent years, organizations have come across complex databases due to the development of technology, the growth of databases and information technologies, and the use of widespread information technologies. If the data found in line with the needs of the institutions are managed successfully and effectively, it is obvious that the institutions and organizations will offer great advantages and opportunities in economic terms. Each action performed in the digital environment leaves a data record behind it. For this purpose, big data, science, data mining and text mining concepts, methods and usage areas were mentioned, and similar and different aspects of data mining and text mining were determined. Through this study, it is expected that the separation of data mining and text mining methods will accelerate the process of strategic decisions of institutions and organizations and provide the required support.

Analysis and modeling of crime text report data has important applications, including refinement of crime classifications, clustering of documents, and feature extraction for spatiotemporal forecasts. Having better neural network representations
of crime text data may facilitate all of these tasks. This paper evaluates the ability of generative adversarial network models to represent crime text data and generate realistic crime reports. We compare four state-of-the-art GAN algorithms in terms of quantitative metrics such as coherence, embedding similarity, negative log-likelihood, and qualitatively based on inspection of generated text. We discuss current challenges with crime text
representation and directions for future research

The objective of this research is to compare movement of stock return literature on companies in the tourism sector and other businesses. This study also analyzed comprehensively the relationship between contexts built on the literature. The mix method approach is adopted by combining text mining methods, which are analyzed using network and content analysis methods. The scientific literature was collected using the web scraping method on Litmaps and selected 200 articles and only used 195 articles (between 2000-2021) related to the topic of stock returns and 23 (between 2013-2020) related to the tourism company sector. In the process of data analysis and visualization, this study uses AntConc, RStudio, and Gephi software. This review discovered that in terms of topic and context, stock return studies discuss issues related to the influence of fundamental factors on stock returns, and vice versa, while the approach used is mostly quantitative based on the financial performance reports. Regarding the frequency of appearance, this result revealed that the effects of fundamental factors on stock returns in tourism companies can be translated into a number of strategic issues, including Current Ratio, TATO, ROE, FATO, and Systematic Risk, which provide a significant and positive effect on Direct Share Prices. Furthermore, the Current Ratio, TATO, ROE, and FATO are thought to have a significant and positive effect on stock returns via Systematic Risk. The scope of this research is limited to the
national journal (Indonesia) on the Litmaps search engine. In theory, the results of this research can be used as a comparison and considerations related to previous studies, as well as what investigation requires to be done immediately, in the context of tourism companies' stock returns. The literature study on the tourism companies’ stock returns issue is perhaps the first systematic literature review in Indonesia.

The textual analysis has become most important task due to the rapid increase of the number of texts that have been continuously generated in several forms such as posts and chats in social media, emails, articles, and news. The management of these texts requires efficient and
effective methods, which can handle the linguistic issues that come from the complexity of natural languages. In recent years, the exploitation of semantic features from the lexical sources has been widely investigated by researchers to deal with the issues of “synonymy and ambiguity” in the tasks involved in the Social Media like document clustering. The main challenges of exploiting the lexical knowledge sources such as 1WordNet 3.1 in these tasks are how to integrate
the various types of semantic relations for capturing additional semantic evidence, and how to settle the high dimensionality of current semantic representing approaches. In this paper, the proposed weighting of features for a new semantic feature-based method as which combined
four things as which is “Synonymy, Hypernym, non-taxonomy, and Glosses”. Therefore, this research proposes a new knowledge-based semantic representation approach for text mining, which can handle the linguistic issues as well as the high dimensionality issue. Thus, the
proposed approach consists of two main components: a feature-based method for incorporating the relations in the lexical sources, and a topic-based reduction method to overcome the high dimensionality issue. The proposed method approach will evaluated using WordNet 3.1 in the text clustering and text classification.

Analysis, Collecting and analyzing online tourist reviews on destinations is important for sustainable tourism. These analyses can give insight into the extent to which natural and cultural assets in the destination are protected. These evaluations should be considered by the authorities as objective and realistic assessments. In this study, 4183 TripAdvisor reviews of foreign tourists visiting "Göreme National Park and Cappadocia Rocky Area", which is listed in the World Heritage Site, were evaluated. The data set consisted of English reviews of foreign tourists visiting the region between the years of 2018-2020. Dictionary-based sentiment analysis, one of the text mining methods, was used in the study. According to the analysis results, the positive perceptions of the tourists about the churches, fairy chimneys, valleys and underground cities in the World Heritage Site were found to be significantly high (75%). Negative evaluations were found to be low (33%). In tourist reviews, 63% positive and 10.49% negative evaluations were made about the protection of the region. In addition, the awareness of the tourists about whether the locations they visit are World Heritage Sites was 30.6%. Tourists explained their negative opinions about the locations they visited with the words "extra_payment", "crowded", and "steep". Another finding of the study was that the area is adequately protected. There were, however, some concerns related to protection. The most striking aspects of protection concerns were those in which the words "painted" and "drawn" were used, an important indicator of visitor sensitivity to the protection of frescoes in rock churches. Since the destruction of natural and cultural assets in the region is unacceptable, recommendations are made to take the necessary measures to prevent such damage.

DC Universe is a fictional universe in which a collection of superheroes and super villains based on characters that appear in comic books by DC Comics is in it. DC Comics itself is the largest and oldest comic book publisher that produces and displays superheroes and super villains. To start a super hero-themed business, there are a number of business examples that can be used as references and can also be used to reap profits from the business, namely, rental of comics, selling merchandise, making clothing lines, making cosplay costumes, making superhero-themed foods, selling action figure. In an effort to start a super hero-themed business, especially the DC Universe theme, it is necessary to pay attention / listen to DC Universe consumers in Indonesia. Classification of opinions or sentiment analysis is one way to find out about a person or group of people towards certain products, services, issues or groups from various social media platforms and the internet. Twitter is one of the social media that is loved by the people of Indonesia. This research tries to utilize what was written by Twitter social media users or better known as a tweet. Tweets will be processed by text mining and processed again using the Naïve Bayes Classifier algorithm.

In this paper, we propose a new approach for evaluating research projects and programs. According our approach, the improvement might be achieved by adopting a results-based and a project portfolio approach, and assuring a research and technology development (RTD) indicators documentation through a standard and comprehensive indicator description, named indicator template. The results-based approach will assure a consistent indicators structure, according to the results chains and a strong connection between ex ante and ex post impact evaluation. The project portfolio approach will assure a tight integration of the research performance indicators, especially between policies goals and program results. And, finally defining a comprehensive indicator template it will be possible to understand better the indicators, develop a detailed analysis, based on the business intelligence techniques, such as OLAP (On-Line Analytical Processing), data mining and text mining. According our knowled...

Named Entities Recognition (NER) has become one of the major issues in Information Retrieval (IR), knowledge extraction, and document classification. This paper addresses a particular case of NER, acronym expansion (or definition) when this expansion does not exist in the document using the acronym. Since acronyms may obviously expand into several distinct sets of words, this paper provides nine quality measures of the relevant definition prediction based on mutual information (MI), cubic MI (MI3), and Dice's coefficient. A combinaison of these statistical measures with the cosine approach is proposed. Experiments have been run on biomedical domain where acronyms are numerous. The results on our biomedical corpus showed that the proposed measures were accurate devices to predict relevant definitions. Povzetek: Predstavljene so metode spletnega preiskovanja dvoumnih akronimov v domeni biomedicinskih baz.

Bibliometric studies have the particularity of analyzing the performance of a research field/area. The present study, following this premise, will map the works of a Postgraduate program at a Brazilian Federal University. For this purpose, text mining and clustering techniques are used to analyze 609 works, including theses and dissertations. The use of text mining techniques and visualization of similarities provided the indication of groupings of knowledge areas. The similarity analysis indicates the little interaction between the fields of knowledge, characterizing the existence of “conceptual islands”. This result may have been impacted by the way the data was extracted in the similarity analysis. The analysis also made it possible to verify which themes arouse greater interest in the program in relation to the research being carried out.

This paper introduces the concept of a classification tool for Web pages called WebClassify, which uses modified naive Bayesian algorithm with multinomial model to classify pages into various categories. The tool starts the classification from downloading training Web text from Internet, preparing the hypertext for mining, and then storing Web data in a local database. The paper also gives an

1 (English) The spread of English as the Lingua Franca of international communication has given rise to meaningful language contact phenomena in the world's languages like loanwords and pseudo-loanwords, namely, words from one language (the donor language) are adopted by another language (the recipient language) sometimes becoming naturalized (Gusmani 1973). From this perspective, it is thus interesting to observe their behaviour in real language use. In particular, this study investigates Anglicisms and pseudo-Anglicisms found in the newspaper discourse of Brexit by way of a bilingual corpus collected from two Italian newspapers, i.e. La Repubblica and Il Corriere della Sera and two British newspapers, i.e. The Independent and The Guardian selected for both their authoritativeness and their extensive readership. The exit of the United Kingdom from the European Union was chosen because it is a widely covered topic both in the Italian and in the British press, thus providing abundant material for comparative analysis, as well as offering useful data in order to explore linguistic variation. It was useful for building an electronic corpus which was retrieved from the digital archives of the newspapers' websites in order to carry out an automated text analysis. The corpus includes articles collected during the periods that both preceded and followed the Brexit referendum. In order to carry out the analysis, corpus-driven methodology was used, namely an approach that lets hypotheses emerge from corpus observation (Tognini-Bonelli 2001). The investigation was carried out by way of the software TalTac2, and the automated text analysis, as a result, turned out to be invaluable in order to investigate and monitor the newspapers' vocabulary which included technical terms from the fields of politics, economics and finance as well as general language words. In order to design and sample a representative corpus, the parameters proposed by Biber (1993) were used to identify descriptive criteria so as to select and balance the population. The aim of this study is to get an overview of the Brexit discourse as used in the two countries' newspapers' vocabulary and terminology (of the two countries) by using text mining to compare and categorize the whole corpus as a collection of texts and, then, to cluster documents on the basis of the lexical similarity of the vocabulary to establish semantic fields or conceptual areas. Furthermore, by way of the lexical and textual analysis, this study also investigates Anglicisms and pseudo-Anglicisms in the Italian newspapers, identifying and analyzing a list of English words used in Italian. The two British newspapers serve as a reference corpus to compare to the list of Anglicisms extracted from the Italian corpus. The articles retrieved from the British newspapers serve to find out which words are typical of each corpus and to identify pseudo-anglicisms, namely new words that seem to be English forms, even though they do not exist in English, or if they do exist, they have a clearly different meaning. Lastly, the data gathered from the bilingual corpus analysis were later compared with other wider corpora included in SketchEngine and on the Brigham Young University platform in order to make generalizations about the distribution of Anglicisms and pseudo-Anglicisms in general language corpora. Abstract 2 (Italian) La diffusione e l'affermazione dell'inglese come lingua franca della comunicazione internazionale ha generato fenomeni significativi di contatto linguistico come i prestiti e i falsi prestiti, ossia parole originariamente nate in una lingua modello che entrano a far parte di un'altra lingua (lingua replica) alla quale vengono talvolta assimilate e adattate (Gusmani 1973). È quindi interessante osservarne l'uso e l'andamento in testi autentici che presentano la lingua nel suo uso corrente. Questo studio analizza gli anglicismi e i falsi anglicismi nel discorso giornalistico della Brexit, attraverso un corpus tratto dai quotidiani italiani La Repubblica e Il Corriere della Sera e dai quotidiani britannici The Guardian e The Independent, che sono stati selezionati per la loro diffusione 2 VALERIA FIASCO JADT 2018 : 14 es Journées internationales d'Analyse statistique des Données Textuelles e la loro autorevolezza. La scelta della tematica dell'uscita del Regno Unito dall'Unione Europea è stata dettata da diversi fattori, tra i quali l'ampia diffusione dell'argomento nella stampa italiana e in quella britannica, dando la possibilità di creare un corpus per realizzare un'analisi comparativa attraverso l'esplorazione della variazione linguistica. Dal momento che queste riviste offrono una versione online che mette a disposizione un archivio digitale consultabile, sono particolarmente adatte per creare un corpus che può essere esaminato attraverso l'analisi automatica del testo. Il corpus è composto da articoli raccolti durante il periodo che precede e segue il referendum della Brexit e la metodologia utilizzata per condurre l'analisi è di tipo corpus-driven, ossia un approccio esplorativo in cui, partendo dall'osservazione del corpus, si arriva alla formulazione delle ipotesi (Tognini-Bonelli 2001). Il software TalTac2 e l'analisi automatica dei testi sono stati estremamente preziosi per esaminare e monitorare il lessico della stampa che include termini tecnici della politica, dell'economia e della finanza, insieme a parole che fanno parte del lessico comune. Per progettare il corpus, sono stati utilizzati i parametri proposti da Biber (1993) con lo scopo di identificare i criteri descrittivi per selezionare e bilanciare la popolazione all'interno del corpus. L'obiettivo di questa ricerca è offrire un'analisi del lessico e della terminologia utilizzata nel discorso sulla Brexit nei quotidiani italiani e inglesi attraverso il text mining per raffrontare i testi che compongono il corpus, categorizzarli e raggrupparli sulla base di somiglianze lessicali per individuare i campi semantici e le aree concettuali. Inoltre, l'analisi lessicale e testuale ha consentito l'identificazione degli anglicismi e dei falsi anglicismi nei quotidiani italiani, mentre il corpus dei quotidiani britannici ha svolto la funzione di corpus di riferimento per paragonare la lista degli anglicismi estratta dal corpus italiano con i dati raccolti nel corpus britannico, capire quali parole sono tipiche di ogni lingua e identificare i falsi anglicismi, vale a dire parole che presentano una forma inglese, che però non esistono nel vocabolario originario o nel caso in cui esistano, il loro significato è completamente differente. Infine, i dati raccolti dall'analisi del corpus bilingue sono stati successivamente confrontati con altri corpora più ampi, consultabili su SketchEngine e sulla piattaforma della Brigham Young University con lo scopo di fare delle generalizzazioni sulla distribuzione degli anglicismi e dei falsi anglicismi in corpora non specialistici.

In this paper, we establish Fog Index (FI) as a text filter to locate the sentences in texts that contain connected biomedical concepts of interest. To do so, we have used 24 random papers each containing four pairs of connected concepts. For each pair, we categorize sentences based on whether they contain both, any or none of the concepts. We then use FI to measure difficulty of the sentences of each category and find that sentences containing both of the concepts have low readability. We rank sentences of a text according to their FI and select 30 percent of the most difficult sentences. We use an association matrix to track the most frequent pairs of concepts in them. This matrix reports that the first filter produces some pairs that hold almost no connections. To remove these unwanted pairs, we use the Equally Weighted Harmonic Mean of their Positive Predictive Value (PPV) and Sensitivity as a second filter. Experimental results demonstrate the effectiveness of our method.

Social media data is increasingly used to gain insights into trends in mental health, but prior studies aimed at confirming a link between online expression of suicidal ideation on social media and actual suicide deaths have been inconclusive. Using comprehensive six-year data sets of Twitter posts and suicide deaths in Japan, we examine the diurnal relationship between the proportional incidence of a suicide-related keyword, “kietai” (“I want to disappear”), and suicide deaths with an OLS regression model. We also use co-occurrence analysis to reveal changes in the linguistic context of the suicide-related keyword at different hours of the day. We find a clear diurnal pattern in the use of this suicide-related keyword, peaking between 1am and 5am. This diurnal trend is positively correlated with suicide deaths among younger cohorts (ages 15 to 44), but the correlation is negative among older adults (45 and over). The correlation among young adults strengthens when a delay between tweet incidence and suicide deaths is included. Compared to daytime tweets, nighttime tweets exhibited a stronger relationship between words related to self-disgust and words directly indicating suicidal intent. This study confirms the hypothesised link between online suicidal ideation and suicide death. Despite frequent flippant use of the keyword, the consistent correlation and the diurnal changes in the context of the keyword's usage demonstrate the value of social media data to the study of mental health trends in groups at risk of suicide.

This study used the data of questionnaires conducted by a local TV program on child rearing to analyze the worries and needs of parents raising children in Yamanashi prefecture. Data from questionnaires conducted by the program for every year between 2007 and 2014 were used, with a total of 1962 respondents. Results from a multiple-choice question revealed that a majority of respondents had worries regarding disciplining their children. Text mining analyses of free-answer questions indicated that respondents are unconfident regarding their methods of reprimanding their children and want to know better ways to respond when their children engage in undesirable behavior. Text mining also revealed that there is strong demand among child rearing parents, mostly mothers, on information regarding child-friendly places to go out to.

This paper describes a text mining tool that performs two tasks, namely document clustering and text summarization. These tasks have, of course, their corresponding counterpart in "conventional" data mining. However, the textual, unstructured nature of documents makes these two text mining tasks considerably more difficult t han their data mining counterparts. In our system document clustering is performed by using

Most of company product or service depends on user experience. So finding feedback of each user is tough but social platform reduce this dependency as people provide this information in form of comments. This paper has work in this domain of sentiment mining where user product based comments were studied for identifying its experience. Paper has extract the phrase feature from the comments dataset. Phrase were arrange in graph where nodes were phrase and edge have similarity sentiment count used in comments. Each phrase was further processed by frog Jump genetic algorithm for clustering of phrases as per sentiment class. In frog jump clustering phrase graph structure was used as fitness value evaluation. Paper has found that memeplex operation of frog algorithm has increase the clustering efficiency of work. Real twitter dataset was used for the comment sentiment analysis. Result shows that Phrase based Frog Jump sentiment detection has increase the accuracy, precision of work as compared to existing algorithm.

Com o crescimento da Educação a distância (EAD), os fóruns de discussão online se apresentam como um importante instrumento no processo de ensino-aprendizagem. No entanto, à medida que as discussões em fóruns crescem torna-se difícil para professores/tutores acompanhar as discussões e consequentemente avaliar as contribuições dos estudantes. Diante disso, este artigo avalia um modelo de avaliação automática de postagens em um fórum educacional conduzido em língua portuguesa por meio de 4 classificadores. Os resultados obtidos demonstram o bom desempenho do multilayer perceptron (MLP) para classificação das postagens e evidencia a possibilidade do modelo avaliado ser utilizado para fins educacionais.

An important task in text mining is finding the dominant topics (and their associated documents) in a collection of documents. While traditional clustering techniques, e.g., hierarchical clustering and K-means, are often used for this task, this paper explores a new clustering algorithm which is based on a shared nearest neighbor approach. Unlike traditional clustering algorithms, not all the data is

The experience of the Golf Practice Class with Lodging and the Change of Life Skills through it

The purpose of this study is to examine the experience of the golf practice class with lodging and the longitudinal change of life skills through it. The subjects of 11 university students (male=9, female=2, mean age = 19.30, SD = 1.17) were asked to answer a questionnaire that was composed of socio-demographic questions, the Experience Scale in University Physical Education Classes (ESUPEC), impressions of golf practice class, and Life Skills Scale for University Students (LSSUS). Descriptive statistics and text mining were conducted to reveal the experience of the golf practice class. Paired t-tests and calculation of Cohen’s d were conducted to examine longitudinal change of life skills. The results of descriptive statistics showed that mean scores of ESUPEC were high compared with a previous research. The results of text mining showed that the words of “enjoy” and “difficult” appeared frequently. In addition, some word networks such as including positive psychological aspects were shown. Paired t-tests revealed that the scores of “Leadership”, “Scheduling” and “Self-Esteem” of LSSUS increased significantly, their Cohen’s d ranged from 0.43 to 0.63. The results above indicate that sports activities with lodging might be more effective to obtain psychosocial skills.

A fully automated, self-driving car can perceive its environment, determine the optimal route, and drive unaided by human intervention for the entire journey. Connected autonomous vehicles (CAVs) have the potential to drastically reduce accidents, travel time, and the environmental impact of road travel. Such technology includes the use of several sensors, various algorithms, interconnected network connections, and multiple auxiliary systems. CAVs have been subjected to attacks by malicious users to gain/deny control of one or more of its various systems. Data security and data privacy is one such area of CAVs that has been targeted via different types of attacks. The scope of this study is to present a good background knowledge of issues pertaining to different attacks in the context of data security and privacy, as well present a detailed review and analysis of eight very recent studies on the broad topic of security and privacy related attacks. Methodologies including Blockchain, Named Data Networking, Intrusion Detection System, Cognitive Engine, Adversarial Objects, and others have been investigated in the literature and problem-and context-specific models have been proposed by their respective authors.

User satisfaction from education institution, health service or other bussiness are often measured using questionnaire with scoring grade system. Generally many comments, advices and suggestions from questionnaire complements is not processed systematically due to lack of method to process. whereas such information might be very useful as additional source in decision making. Opinion mining and sentiment analysis are well suited to address the issue. The objective of this study is to develop sentiment classifacation system using Naive Bayes Classifier (NBC) and Support Vector Machine (SVM). Opinion to be classified was Indonesian textual comments from academic questionnaire. Data contained of 1000 comments that was sampled as multi domain opinion, represented many objects such as lecturer, class room, etc. Features used for classification was selected from word and phrasa using in the comments. The weighting scheme that we used was TF/IDF. The results show that SVM gives good performance as compared with NBC.

The Indonesian tourism industry continues to develop and has become the core of the nation economy. Indonesia is known for its wealth of natural beauty, which can be used as a potential for tourism business. Garut is a city in Indonesia known for its beach, mountains, and culinary arts. The purpose of this study is to create a priority map of tourist attractions that can be utilized by local governments. Sentiment analysis was used 413,175 netizen comments via the social media platforms Instagram and Google reviews. Data was collected from January 2018-February 2019. The results show that the number of positive comments is significantly higher than the number of negative comments. Beach tourism a serious priority; not only is it the most preferred tourist attraction, but also the type that gets the most negative comments. While the main problem for Garut Regency tourism is hygiene, garbage is either overlapping or scattered, preventing Garut from having all-around tourism charm instead of being superior only in the sector of natural beauty. The suggestions in this research can be used as proposals for improving and developing tourism to realize a dignified 'Garut Charm'.

In the present state of digital world, computer machine do not understand the human's ordinary language. This is the great barrier between humans and digital systems. Hence, researchers found an advanced technology that provides information to the users from the digital machine. However, natural language processing (i.e. NLP) is a branch of AI that has significant implication on the ways that computer machine and humans can interact. NLP has become an essential technology in bridging the communication gap between humans and digital data. Thus, this study provides the necessity of the NLP in the current computing world along with different approaches and their applications. It also, highlights the key challenges in the development of new NLP model.