Text mining has become one of the trendy fields that has been incorporated in several research fields such as computational linguistics, Information Retrieval (IR) and data mining Natural Language Processing (NLP) techniques were used to extract knowledge from the textual text that is written by human beings Text mining reads an unstructured form of data to provide meaningful information patterns in a shortest time period Social networking sites are a great source of communication as most of the people in today’s world use these sites in their daily lives to keep connected to each other It becomes a common practice to not write a sentence with correct grammar and spelling This practice may lead to different kinds of ambiguities like lexical, syntactic, and semantic and due to this type of unclear data, it is hard to find out the actual data order Accordingly, we are conducting an investigation with the aim of looking for different text mining methods to get various textual orders on social media websites This survey aims to describe how studies in social media have used text analytics and text mining techniques for the purpose of identifying the key themes in the data This survey focused on analyzing the text mining studies related to Facebook and Twitter; the two dominant social media in the world Results of this survey can serve as the baselines for future text mining research

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A survey of text mining in social media: Facebook and Twitter perspectives

Nowadays, research in text mining has become one of the widespread fields in analyzing natural language documents. The present study demonstrates a comprehensive overview about text mining and its current research status. As indicated in the literature, there is a limitation in addressing Information Extraction from research articles using Data Mining techniques. The synergy between them helps to discover different interesting text patterns in the retrieved articles. In our study, we collected, and textually analyzed through various text mining techniques, three hundred refereed journal articles in the field of mobile learning from six scientific databases, namely: Springer, Wiley, Science Direct, SAGE, IEEE, and Cambridge. The selection of the collected articles was based on the criteria that all these articles should incorporate mobile learning as the main component in the higher educational context. Experimental results indicated that Springer database represents the main source for research articles in the field of mobile education for the medical domain. Moreover, results where the similarity among topics could not be detected were due to either their interrelations or ambiguity in their meaning. Furthermore, findings showed that there was a booming increase in the number of published articles during the years 2015 through 2016. In addition, other implications and future perspectives are presented in the study.

Using Text Mining Techniques for Extracting Information from Research Articles

With the development of social media, customers are sharing their experiences, and it is rapidly spreading as a form of online review. That is why the online review has become a significant information source affecting customers’ purchase intention and behavior. Therefore, it is important to understand the customer’s experience shown in the online review in order to maintain sustainable customer satisfaction and loyalty. The purpose of this study is to investigate what are the key attributes and the structural relationship of those key attributes. To accomplish this purpose, a total of 6596 hotel reviews were collected from Google (google.com). A frequency analysis using text mining was performed to figure out the most frequently mentioned attributes. In addition, semantic network analysis, factor analysis, and regression analysis were applied to understand the experience and satisfaction of the hotel customer. As a result, the top 99 keywords were divided into four groups such as “Intangible Service”, “Physical Environment”, “Purpose”, and “Location”. The factor analysis reduced the dimension of the original 64 keywords to 22 keywords, and grouped them into five factors, which are “Access”, “F&B (Food and Beverage)”, “Purpose”, “Tangibles”, and “Empathy”. Based on these results, theoretical and practical implications for sustainable hotel marketing strategies are suggested.

https://www.mdpi.com/2071-1050/11/23/6570/pdf

Investigating Key Attributes in Experience and Satisfaction of Hotel Customer Using Online Review Data

A critical review of text-based research in construction: Data source, analysis method, and implications

With the rapid growth of information technology, the amount of unstructured text data in digital libraries is rapidly increased and has become a big challenge in analyzing, organizing and how to classify text automatically in E-research repository to get the benefit from them is the cornerstone. The manual categorization of text documents requires a lot of financial, human resources for management. In order to get so, topic modeling are used to classify documents. This paper addresses a comparison study on scientific unstructured text document classification (e-books) based on the full text where applying the most popular topic modeling approach (LDA, LSA) to cluster the words into a set of topics as important keywords for classification. Our dataset consists of (300) books contain about 23 million words based on full text. In the used topic models (LSA, LDA) each word in the corpus of vocabulary is connected with one or more topics with a probability, as estimated by the model. Many (LDA, LSA) models were built with different values of coherence and pick the one that produces the highest coherence value. The result of this paper showed that LDA has better results than LSA and the best results obtained from the LDA method was ( 0.592179 ) of coherence value when the number of topics was 20 while the LSA coherence value was (0.5773026) when the number of topics was 10.

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LSA & LDA topic modeling classification: comparison study on e-books

In recent years growth of digital data is increasing, knowledge discovery and data mining have attracted great attention with coming up need for turning such data into useful information and knowledge. The use of the information and knowledge extracted from a large amount of data benefits many applications like market analysis and business management. In many applications database stores information in text form so text mining is the one of the most resent area for research. To extract user required information is the challenging issue. Text Mining is an important step of knowledge discovery process. Text mining extracts hidden information from notstructured to semi-structured data. Text mining is the discovery by automatically extracting information from different written resources and also by computer for extracting new, previously unknown information. This survey paper tries to cover the text mining techniques and methods that solve these challenges. In this survey paper we discuss such successful techniques and methods to give effectiveness over information retrieval in text mining. The types of situations where each technology may be useful in order to help users are also discussed.

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Text Mining Methods and Techniques

Due to rapid growth of digital data usages in recent database applications it is required to utilize effective text mining method for efficient performance. Text mining is research area because in many applications database content is not only in form of numerical data but also textual data form. To exploit hidden information from non- structured to semi-structured data form text mining is applicable. The challenging issue in text mining is to extract user required information in effectual manner. To perform this task text mining methods are used. Text mining methods categorization is based on how text document are analyzed. In these methods text document analyzed on the basis of term, phrase, concept and pattern. The pattern based approach can improve the accuracy of system for evaluating term weights because discovered patterns are more specific than whole documents. Objective is to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Proposed system increases efficiency of pattern discovery using different data mining algorithms with pattern deploying and pattern evolving method to solve misinterpretation and low frequency problem. Performance enhancement is achieved by applying multithreaded approach and concept formation from effectively discovered patterns.

Sonali Vijay Gaikwad

Papers

Text Mining Methods and Techniques

Performance Enhancement for Effective Pattern Discovery & Concept Formation in Text Mining