Nowadays, a huge amount of available information on the web is Text documents and articles. Text Mining is a way to extract unstructured and semi-structured information from this available information on the Internet and Also, Mining process of the Text of knowledge and unknown, incomprehensible and potential patterns among the multitude of datasets. This research is a type of library studies. Although Text Mining methods are mostly based on Latin sources, but by searching Persian databases, we have found over the past decade, the subject of Text Mining has become doubly important for Iranian researchers, especially students of computer science and information technology,So that a significant part of the conference papers related to computer science and technology are articles related to this field. Research findings show that Text Mining is an application of data Mining and the main difference between them is: the extraction of patterns from Text with natural language in Text Mining, while data Mining operates on structured databases. Text Mining processes have two main phases: document preprocessing and knowledge extraction. So far, eight techniques have been introduced for Text Mining which are: Information extraction, information retrieval, Text summarization, classification, clustering, visualization, natural language processing and belief Mining. In recent years, much attention has been paid to Text Mining in the international and national spheres. The dramatic increase in Textual data has prompted researchers to look for ways to explore this data. Naturally, Iranian researchers have been no exception. Text Mining, with all its methods and techniques, is an effort to assist researchers in extracting useful and valuable knowledge and information from the mass of unstructured Texts scattered throughout the Internet.

The main goal of this research is to predict financial distress in terms of key words and phrases using the Text Mining technique. The statistical population of this research is the audit reports of 50 financially disadvantaged companies listed in Tehran Stock Exchange in 2017. Therefore, these companies were subjected to Text Mining in order to collect the required data for the research reports from the years 2015 and 2016. Research findings indicate that the term non-observance of regulations and rules with 162 cases, the word conflicts with 123 cases, the absence of the presence of the beneficiary's manager with 122, loss of profit, decrease in the value of goods and assets with 116 items, lack of access to information And documents with 102 cases, adjustments to 101 cases, bonds of directors with 93 cases, board approvals of 86, and ultimately accumulated losses and renewals with 85 views in audit reports. Given the extracted terms and expressions, the method of Text Mining can be used to predict corporate financial distress. It can also be used as a method to extract useful information from audit reports.

Text Mining’ refers to the computational process of unstructured Text analytics for extracting latent linguistic layers and themes. It is especially significant as content or thematic analysis in descriptive and interpretive studies. This process begins with structuring simple Texts and proceeds with summarizing, classifiing, modelling, evaluating and interpreting the inherent Textual concepts and patterns. Given that this method counts as an interdisciplinary innovation especially in discoursal studies, it is to be pursued more intensively in academic studies. Despite the multitude of English studies in this area, there has been little interest to date in Text Mining amongst Iranian researchers as evidenced by the critically limited number of local Persian and English studies. Thus looking into the theory and practice of Text Mining and its major analytic tools and methods in Persian and English, this paper aims to prepare the ground for utilizing this methodology in language studies.

Introduction: The word agility identified the speed and the power of responses during facing with organization internal and external matters. The health care organizations must be agile like any other organization in today fast speeding world, because being agile is an additional advantage in the competitive world. In this paper the organizations' agility, data Mining, Text Mining, and the role of all these tools that may have provide the knowledge and the move of the healthcare organizations toward the agility, will be preceded.Literature review: Specialist in the Spinal Disorders Hospital in south California in Los Angeles use data Mining process to discover different factors affecting on success or failure of the spinal surgeries operations causing improvement in health care. And also the financial organization for healthcare; and Medicare and Medicaid using TextMining and Data-Mining to discover any fraud or misuses in insurances and different type of the health care operations.Conclusion: Information is the most important tool in the management. Converting information to the knowledge has a key role in moving organizations toward agility. By using the analytical tools in organizations, the new knowledge in medical field on top of the information about the processes, patterns, and treatment results to upgrades the quality of the health care, could be achieved, and by passing information about weakness and strength points, the threat, the opportunities and technology changes; to managers, they could be able to plan toward the agility.

Experience is defined as the interaction between the individual and the environment and internal response to this interaction. This internal interaction includes emotions and thoughts that follow the sensory perception of the environment. People's experience in hospitality and hoteling is a collection of evaluations of different stages of service in various interactions and consumption of products and services. Therefore, understanding the experience points of hotel guests provides the basis of providing appropriate services and products for them and leads to the competitiveness of hotels. Thus, this study aimed to map the thematic networks of hotel guests' experience using Text Mining techniques in 4-star hotels in Tehran city. This research has a qualitative approach and is applied in purpose and descriptive-analytical in the data collection method. Research data is collected using a crawler from the TripAdvisor site from January 2019 to September 2020. The statistical population of this study is all 4-star hotels in Tehran city. The sampling method in this research is random sampling. The sample of this research includes Espinas Palace hotel (1413 Comments), Parsian Esteghlal (342 comments), Laleh hotel (222 comments), Homa hotel (134 comments), and Azadi Grand hotel (82 comments). For analyzing the research data, the first Text Mining techniques and then theme analysis is used. The Text Mining process consists of five steps: tokenize, transform cases, filter step words, stem (snowball), and generate n-gram (terms). Also, the theme analysis consists of six steps: familiarity with the Text, initial coding, identify themes, construct thematic networks, interpret thematic networks and represent reports. So as to evaluate the validity, content validity and internal and external validity have been used. In this study, the process of data preprocessing has been done to ensure content validity. In order to evaluate the internal validity, the criterion of information richness, and the external validity, the theoretical replication criterion have been used. For assessing reliability in this study, a structured process has been used to record, write and interpret the data. The research's thematic networks include six themes: physical environmental experience in the hotel, experience related to the staff in the hotel, social communication experience in the hotel, food experience in the hotel, location experience in the hotel, and emotional experience in the hotel. From a practical view, this research suggests hotel managers have the necessary plans in the field of dimensions of guests' experience and provide services related to these dimensions in tourist experience points to meet their expectations. Finally, suggestions for future research as well as research limitation are made.

In this paper, we firstly review some definitions related to fractional calculus and fractional entropy, as a generalization of Shannon entropy. Then we introduce the generalized word importance metric based on fractional entropy. Using the proposed definition, we introduce a new Text Mining method based on fractional entropy. This method for keyword extraction of the Statistical Inference book by Casella and Berger (1990) shows that the F-measure value of the proposed Text Mining method, is higher than the related value for common Text Mining method based on Shannon entropy. These results indicate that the proposed Text Mining method based on fractional entropy is more comprehensive than the traditional Text Mining based on Shannon entropy.

What is clear is that judges usually judge cases based on their knowledge, experience, personality, and sentiment. Due to high pressures and stress, it may be difficult for them to carefully examine documents and evidence, which leads to more subjective judgments. Legal judgment prediction with artificial intelligence algorithms can benefit judicial bodies, legal experts, and litigants as well as judges. In this research, we are looking at predicting legal sentences in drug cases involving the purchase, possession, concealment, or transportation of illicit drugs, using machine learning methods, and the effect of sentiment and emotions in case Texts on predicting the severity of whipping, fines, and imprisonment. So, the Text documents of 6000 Persian drug-related cases were pre-processed and then the translation of the NRC Glossary of Emotions and sentiment was used to give each item a score for positive or negative sentiment and a score for emotion. Then machine learning methods were used for modeling. BERT, TFIDF+Adaboost, and Skipgram+LSTM+CNN methods had the highest accuracy, respectively. Also, evaluation criteria were analyzed in situations where sentiment scores, emotional scores, or both were used in the prediction process along with judicial Texts. Finally, it was found that the use of sentiment and emotion scores improves the accuracy of legal judgment predictions for all three types of sentences and that sentiments have a greater impact on the accuracy of legal judgment predictions than emotions