A SURVEY PAPER FOR TEXT MINING OF IMPORTANT TERM FROM RELEVANCE DOCUMENT USING PATTERN BASED MODEL

The quality of extracted features is the key issue to text mining due to the large number of terms, phrases, and noise. Most existing text mining methods are based on term-based approaches which extract terms from a training set for describing relevant information. However, the quality of the extracted terms in text documents may be not high because of lot of noise in text. For many years, some researchers make use of various phrases that have more semantics than single words to improve the relevance, but many experiments do not support the effective use of phrases since they have low frequency of occurrence, and include many redundant and noise phrases. In this paper, we propose a novel pattern discovery approach for text mining. This approach first discovers closed sequential patterns in text documents for identifying the most informative contents of the documents and then utilise the identified contents to extract useful features for text mining. We develop a novel fusion method based on Dempster-Shafer's evidential reasoning which allows to combine the pieces of document to discover the knowledge (features). To evaluate the proposed approach, we adopt the feature extraction method for information filtering (IF). The experimental results conducted on Reuters Corpus Volume 1 and TREC topics confirm that the proposed approach could achieve excellent performance.

It is a big challenge to develop effective methods that can discover high quality and useful features in text documents. Most existing information retrieval and text mining methods focuses on term-based approach that often suffers from the problems of term variation and noise. This paper illustrates an innovative approach that discovers relevant knowledge to precisely describe text features for retrieving web information. In particular, it extracts precise text patterns by considering both relevant and irrelevant documents. Then, the discovered patterns are used to find accurate relevant features in a training set. The proposed approach has been evaluated through the implementation of a novel information filtering model and a comparative evaluation is conducted by invoking state-of-the-art models. The experimental results obtained based on the Reuters Corpus Volume 1 and TREC topics show that the proposed approach significantly outperforms the best baseline method.

PurposeIn business, online reviews have an economic impact on firm performance. Customers’ data in the form of online reviews was used to understand the appreciation and service complaints written by previous customers. The study is an analysis of the online reviews written by the customers about Indian heritage hotels. This study aims to understand the dimensions of service appreciation and service complaints by comparing positive- and negative-rated reviews and find the patterns in the determinants of the satisfaction and dissatisfaction of the customers.Design/methodology/approachA total of 23,643 online reviews about heritage hotels were collected from the TripAdvisor website by using a Web crawler developed in Python. A total of 1000 reviews were randomly selected for further analysis to eliminate the bandwagon effect. Unsupervised text mining techniques were used to analyze reviews and find out the interesting patterns in text data.FindingsBased on Herzberg two-factor theory, this study found satisfied and dissatisfied determinants separately. The study revealed some common categories discussed by satisfied and dissatisfied customers. The factors which satisfy the customers may also dissatisfy the customers if not delivered properly. Satisfied customers mentioned about tangible features of the hotel stay, which includes physical signifiers, traditional services, staff behavior and professionalism and core products (rooms, food). However, most of the customers complained about intangible service problems, such as staff attitude, services failure, issues with reservation and food, value for money and room condition. The results are contradicting with commercial hotels-based studies owing to the unique services provided by heritage hotels.Practical implicationsThe dimensions for satisfaction and dissatisfaction among customer of heritage hotels provide marketers to understand the real emotion and perception of the customers. As these dimensions were extracted through text mining of the reviews written by the customer of heritage hotels, the results would certainly give better insights to the hotel marketers.Originality/valueThe study is a rare attempt to study online reviews of customers on heritage hotels through a text mining approach and find the patterns in the behavior and the determinants of satisfaction and dissatisfaction of customers.

Due to large size words also data patterns, it is difficult to ensure the quality of relevant characteristics that are found in text documents that describe user preferences. Most widely used text mining and classification techniques now in use have embraced term-based strategies. However, polysemy and synonymy issues have affected them all. The theory that pattern-based approaches should outperform term-based ones in performance in expressing user preferences has been often held throughout the years, however text mining still struggles with how to employ large-scale patterns successfully. This research introduces a novel methodology for relevance feature discovery to address this hard problem. It finds higher level features in text texts that are both positive and negative patterns and uses them instead of low-level features (terms). Additionally, it organised terms into categories and updates term weights according to the patterns and specificity of those distributions. Significant tests employing this model on the datasets RCV1, TREC themes, and Reuters-21578 reveal that it performs noticeably better than both the most advanced term-based approaches and pattern-based methods.

News is new information or information about something that is currently happening, presented in the form of print media, broadcasts, social media or word of mouth to third parties or multiple people. The spread of this news is now very much happening both from social media or messages. However, the problem is that no one can guarantee the truth of the news received. Text mining is the application of data mining concepts and techniques to look for patterns in text, the process of analyzing text to find useful information for a particular purpose. Another definition related to text mining is that text mining is data mining in the form of text where the data source is usually obtained from documents and the goal is to find words that can represent the contents of the document so that an analysis of the connectivity between documents can be carried out. To make it easier to identify news hoax requires a text mining algorithm, using a text mining algorithm can be useful to get real news. Based on the calculations of the TF-IDF algorithm, it can be concluded that the similarity of the meaning of the news whose truth is not yet known is closest to the news ofmerahputih.com with a confidence level of 23.11%.

Gigantic growth of text mining is becoming a potential source of crowd wisdom extraction and analysis especially in terms of text pre-processing and analysis. The ubiquitous growth in the text mining field is unprecedented where social media mining is playing a significant role. Therefore, it’s a significant task of current research purview which not only provides an appropriate platform for the contributors but also for active information seekers. Text mining has gained the widespread attention of internet users which is almost on an explosion level. Accessibility of data on such a behemoth scale mandates regular and critical analysis of this information for various perspectives’ plausibility. Rich patterns of online social text can be exploited to extract relevant information effectively. As text mining plays a significant and crucial role in discovery of these insights, therefore its challenges and contribution to social media analysis must be explored extensively. In this chapter, we provide a brief summary of the modern state of text mining in pre-processing and machine learning mechanism for COVID-19. Emphasis is placed on the resources and learning mechanism available to researchers and practitioners, as well as the major text mining tasks of interest to the community. Thus, the main aim of this chapter is to expound and intellectualize the domains of social media which are accessible on an extraordinary range in the field of text mining for COVID-19.

BackgroundVast numbers of domestic violence (DV) incidents are attended by the New South Wales Police Force each year in New South Wales and recorded as both structured quantitative data and unstructured free text in the WebCOPS (Web-based interface for the Computerised Operational Policing System) database regarding the details of the incident, the victim, and person of interest (POI). Although the structured data are used for reporting purposes, the free text remains untapped for DV reporting and surveillance purposes.ObjectiveIn this paper, we explore whether text mining can automatically identify mental health disorders from this unstructured text.MethodsWe used a training set of 200 DV recorded events to design a knowledge-driven approach based on lexical patterns in text suggesting mental health disorders for POIs and victims.ResultsThe precision returned from an evaluation set of 100 DV events was 97.5% and 87.1% for mental health disorders related to POIs and victims, respectively. After applying our approach to a large-scale corpus of almost a half million DV events, we identified 77,995 events (15.83%) that mentioned mental health disorders, with 76.96% (60,032/77,995) of those linked to POIs versus 16.47% (12,852/77,995) for the victims and 6.55% (5111/77,995) for both. Depression was the most common mental health disorder mentioned in both victims (22.25%, 3269) and POIs (18.70%, 8944), followed by alcohol abuse for POIs (12.19%, 5829) and various anxiety disorders (eg, panic disorder, generalized anxiety disorder) for victims (11.66%, 1714).ConclusionsThe results suggest that text mining can automatically extract targeted information from police-recorded DV events to support further public health research into the nexus between mental health disorders and DV.

Sequential pattern is an important research topic in data mining and knowledge discovery. Sequential pattern is traditionally formed as (A, B) where A and B are frequent sequence in a transaction database. We extend this definition to include sequential patterns of forms (A, notB), (notA, B) and (notA, notB), which present negative sequential patterns among sequences. We call patterns of the form (A, B) positive sequential patterns, and patterns of the other forms negative patterns. Negative sequential patterns can also provide very useful insight view into the data set although they are different from positive ones. We put forward a discovery algorithm for mining negative sequential patterns from large transaction database in this paper.

It is of great significance to establish an assessment model for organ failures in the early stage of admission in acute pancreatitis (AP). And the clinical notes are underutilized. To predict organ failures for AP patients using early clinical notes in hospital, early text features obtained from the pretrained Chinese Bidirectional Encoder Representations from Transformers model and attention-based LSTM were combined with early structured features (laboratory tests, vital signs, and demographic characteristics) to predict organ failures (respiratory, cardiovascular, and renal) in 12,748 AP inpatients in West China Hospital, Sichuan University, from 2008 to 2018. The text plus structured features fusion model was used to predict organ failures, compared to the baseline model with only structured features. The performance of the model with text features added is superior to the model that only includes structured features.

In data mining field, sequential pattern mining can be applied in divers applications such as basket analysis, web access patterns analysis, and quality control in manufactory engineering, etc. Many methods have been proposed for mining sequential patterns. However, conventional methods only consider the occurrences of itemsets in customer sequences. The sequential patterns discovered by these methods are called as positive sequential patterns, i.e., such sequential patterns only represent the occurrences of itemsets. In practice, the absence of a frequent itemset in a sequence may imply significant information. We call a sequential pattern as negative sequential pattern, which also represents the absence of itemsets in a sequence. The two major difficulties in mining sequential patterns, especially negative ones, are that there may be huge number of candidates generated, and most of them are meaningless. In this paper, we proposed a method for mining strong positive and negative sequential patterns, called PNSPM. In our method, the absences of itemsets are also considered. Besides, only sequences with high degree of interestingness will be selected as strong sequential patterns. An example was taken to illustrate the process of PNSPM. The result showed that PNSPM could prune a lot of redundant candidates, and could extract meaningful sequential patterns from a large number of frequent sequences.

The purpose of the research was to assess mental health of people suffering from connective tissue diseases, both in negative terms, i.e., the presence of disorders, and in positive terms - patients'well-being. Due to the specificity of connective tissue diseases, among others their unpredictable course and the pain that often accompanies patients, selected clinical and sociodemographic variables were included in the analyses. 101 people participated in the study, 81 women and 20 men aged 18 to 57 years, suffering from various connective tissue diseases. Some people completed questionnaires during support group meetings (38.6%), others via the internet. The HADS-M scale was used to examine indicators of mental health in negative terms, the PERMA-Profiler questionnaire was used to assess well-being (an indicator of mental health in positive terms), and the authors' own survey was used to obtain clinical and sociodemographic data. Subjects suffering from connective tissue diseases are characterized by high severity of anxiety and depressive symptoms. Relationships between mental health and selected clinical and sociodemographic variables were observed, e.g., greater pain is associated with higher rates of anxiety and depression, and lower well-being; the level of well-being increases with age. It is important to monitor the mental health of people with chronic somatic diseases, including connective tissue diseases, especially in young people at the initial period of the disease, suffering from severe pain, residents of villages and small towns where access to support is limited.

Mining frequent sequential patterns from sequence databases has been a central research topic in data mining and various efficient mining sequential patterns algorithms have been proposed and studied. Recently, a novel sequential pattern mining research, called mining repetitive gapped subsequences, has attracted the attention of many researchers. However, the number of repetitive gapped subsequences generated by even these closed mining algorithms may be too large to understand for users, especially when support threshold is low. In this paper, we propose the problem of how to compress repetitive gapped sequential patterns. A novel distance measure of repetitive gapped sequential patterns and an efficient representative pattern checking scheme, δ-dominate sequential pattern checking are proposed. We also develop an efficient algorithm, CRGSgrow ( Compressing Repetitive Gapped Sequential pattern grow), including an efficient pruning strategy, SyncScan. An empirical study with both real and synthetic data sets clearly shows that the CRGSgrow has good performance.Keywordsrepetitive gapped sequential patterncompressing frequent patterns

The joint effect of framing and anchoring on internet buyers’ decision-making

11 - Convergence

The task of classifying news manually requires in-depth knowledge of the domain and expertise to spot anomalies in the text. During this research, we discussed the matter of classifying fake news articles using machine learning models and ensemble techniques. The info we used in our work is collected from the World Wide Web and contains news articles from various domains to cover most of the news rather than specifically classifying political news. The first aim of the research is to identify patterns in text that differentiate fake articles from true news. Within the proposed system we will extract different textual features from the articles using a machine learning tool and used the feature set as an input to the models. the training models were trained and parameter-tuned to obtain optimal accuracy. Some models have achieved relatively higher correctness than others. we'll use multiple performance metrics to compare the results for each algorithm. The ensemble learners have shown an whole better score on all presentation metrics as related to the separable beginners.