scholarly journals User Needs Search Using the Text Mining - From Commodity Comparison, Understanding the Difference of Users Awareness

2014 ◽  
pp. 461-467
Author(s):  
Yukiko Takahashi ◽  
Yumi Asahi
Keyword(s):  
Text Mining ◽  
User Needs ◽  
The Difference

scholarly journals Forecasting Net Income Estimate and Stock Price Using Text Mining from Economic Reports

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 292 ◽  
Author(s):  
Masahiro Suzuki ◽  
Hiroki Sakaji ◽  
Kiyoshi Izumi ◽  
Hiroyasu Matsushima ◽  
Yasushi Ishikawa
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Stock Prices ◽  
Stock Price ◽  
Net Income ◽  
The Difference

This paper proposes and analyzes a methodology of forecasting movements of the analysts’ net income estimates and those of stock prices. We achieve this by applying natural language processing and neural networks in the context of analyst reports. In the pre-experiment, we applied our method to extract opinion sentences from the analyst report while classifying the remaining parts as non-opinion sentences. Then, we performed two additional experiments. First, we employed our proposed method for forecasting the movements of analysts’ net income estimates by inputting the opinion and non-opinion sentences into separate neural networks. Besides the reports, we inputted the trend of the net income estimate to the networks. Second, we employed our proposed method for forecasting the movements of stock prices. Consequently, we found differences between security firms, which depend on whether analysts’ net income estimates tend to be forecasted by opinions or facts in the context of analyst reports. Furthermore, the trend of the net income estimate was found to be effective for the forecast as well as an analyst report. However, in experiments of forecasting movements of stock prices, the difference between opinion sentences and non-opinion sentences was not effective.

scholarly journals Classification of Biomedical Literature in Hypertension and Diabetes

2020 ◽  
Vol 1 (2) ◽  
pp. 114-119
Author(s):  
Nur Aniq Syafiq Rodzuan ◽  
Shahreen Kasim ◽  
Mohanavali Sithambranathan ◽  
Muhammad Zaki Hassan
Keyword(s):  
Text Mining ◽  
New Technology ◽  
Biomedical Literature ◽  
Text Documents ◽  
Textual Databases ◽  
The Difference ◽  
Classification Evaluation ◽  
Linguistic Approaches ◽  
Clear Information

Textual information gives us more clear information as it is presented using words and characters, which is easy for humans to understand. To extract this kind of information, text mining was introduced as new technology. Text mining is the process of extracting non-trivial patterns or knowledge from text documents or from textual databases. The purpose of this research paper is to perform and compare keyword extraction using statistical and linguistic extraction tools for 120 text documents related to hypertension and diabetes disease. In order to draw this comparison, RStudio, a statistical-based tool and TerMine, a linguistic-based tool have been used to demonstrate the process of extracting the specified keyword from the biomedical literature. Thus, classification evaluation using Naïve Bayes classifier is carried out in order to evaluate and compare the performance of the statistical and linguistic approaches using these tools. Experimental results show the result of the comparison and the difference between both tools in executing extraction keywords.

Commodity Recommendation System of E-Commerce Based on Classification Users

2014 ◽  
Vol 556-562 ◽  
pp. 6689-6692 ◽  
Author(s):  
Jie Chen
Keyword(s):  
Electronic Commerce ◽  
Recommendation System ◽  
User Needs ◽  
Single Strategy ◽  
The Difference ◽  
Recommendation Strategy

With the development of electronic commerce, commodity recommendation system of using a single strategy for all users has been unable to meet the needs of businesses and consumers. This paper proposed a commodity recommendation strategy of e-commerce based on classification users, e-commerce users are classified as new users, general users, Silver users and Gold users. According to the difference of users, the paper recommended using different methods to improve the efficiency of commodity recommendation system to better meet user needs.

scholarly journals A Problem Well Defined is Nearly Solved

2010 ◽  
Vol 4 (2) ◽  
Author(s):  
Ryan Lewis
Keyword(s):  
Medical Device ◽  
Design Process ◽  
Unmet Needs ◽  
User Needs ◽  
Clear Understanding ◽  
Device Design ◽  
Medical Device Design ◽  
Consulting Firms ◽  
Design Projects ◽  
The Difference

Solving problems is simple. Understanding which problems should be solved is immensely challenging. Medical device design projects frequently begin with a search for solutions rather than a clear appraisal of needs. Understanding clinical needs, user needs, unmet needs and the differences between them is crucial. Through a process of filtering research into an array of needs the author prescribes a method to aid the designer in defining the overarching problem. This array can create a clear path to problem solutions while utilizing a methodology that fits in the context of a regulated design process. For medical device startups, design consulting firms, and corporate manufacturers, a clear understanding of the problem can mark the difference between a misguided solution and a solution that benefits physicians and patients.

scholarly journals Integrating Text Mining and Balanced Scorecard Techniques to Investigate the Association between CEO Message of Homepage Words and Financial Status: Emphasis on Hospitals

2021 ◽  
Author(s):  
Hyung Jong Na ◽  
Kun Chang Lee ◽  
Sung Tae Kim
Keyword(s):  
Text Mining ◽  
Balanced Scorecard ◽  
Financial Information ◽  
Hospital Organization ◽  
Financial Ratio ◽  
Financial Status ◽  
The Balanced Scorecard ◽  
Financial Strength ◽  
The Difference ◽  
Future Vision

(1) Background: The CEO message of hospital homepage contain various contents such as the hospital's future vision, promises with customers, upgraded services and public activities. The CEO’s message of the homepage includes non-financial information as well as financial information of corporates. Also, it provides useful information for not only company's goals and vision but also firm performance and strategies for the future. This study aims to investigate associations between CEO’s message of hospitals homepages and financial status. We used the balanced scorecard frame to analyze what content on the hospital's homepage is related to the hospital's various financial ratios. (2) Methods: We adopt a text mining method to extract significantly repeated keywords from the CEO’s message of hospital website. And we classify these keywords by a balanced scorecard frame. To examine the relationship between keywords of CEO’s message of the hospital homepage and hospital’s financial ratio, T-test is conducted for the difference in the TF-IDF (Term Frequency is Divided by Inverse Document Frequency) mean of the home page contents and its relationship with the views of the balanced scorecard framework. (3) Results: According to empirical results on 65 samples collected from local hospitals, there are some significant relationship between the qualitative content of the hospital's homepage and the quantitative financial ratio that indicates profitability, activity, leverage, liquidity, and transfer to essential business fund (EBF) income. (4) Conclusions: The introduction section of a homepage is most accessible to customers, containing the aims and ideals of hospitals and reflecting their values and visions [1]. In addition, in view of financial status, they can either emphasize financial strength or focus on other areas to mask weakness of financial information. This study reminds us of the importance of hospital website’s disclosure, and it can be inferred from the financial status of the hospital. It also highlights the need for harmonization between quantitative data, financial statements, and qualitative data, CEO’s messages. (5) Implications: To our best knowledge, this paper is the first research attempting to investigate the relation between text of hospital homepage and financial ratio of hospital through text mining technique and balanced scorecard frame. Hospitals take a crucial part in a country’s welfare and healthcare backbone industry. Nevertheless, in many countries, hospital organization sectors tend to remain a source of critical fiscal deficits due to its ineffective and sloppy management. We expect that the result of this paper can provide hospital managers to useful information.

Topic Maps Generation by Text Mining

2011 ◽  
pp. 1130-1134
Author(s):  
Hsin-Chang Yang ◽  
Chung-Hong Lee
Keyword(s):  
Text Mining ◽  
Search Engine ◽  
Information Resources ◽  
User Needs ◽  
Specific Information ◽  
Specific Domain ◽  
Topic Maps ◽  
Finite Amount ◽  
Reasonable Result ◽  
Topic Map

Topic maps provide a general, powerful, and user-oriented way to navigate the information resources under consideration in any specific domain. A topic map provides a uniform framework that not only identifies important subjects from an entity of information resources and specifies the resources that are semantically related to a subject, but also explores the relations among these subjects. When a user needs to find some specific information on a pool of information resources, he or she only needs to examine the topic maps of this pool, select the topic that seems interesting, and the topic maps will display the information resources that are related to this topic, as well as its related topics. The user will also recognize the relationships among these topics and the roles they play in such relationships. With the help of the topic maps, you no longer have to browse through a set of hyperlinked documents and hope that you may eventually reach the information you need in a finite amount of time, while knowing nothing about where to start. You also don’t have to gather some words and hope that they may perfectly symbolize the idea you’re interested in, and be well-conceived by a search engine to obtain reasonable result. Topic maps provide a way to navigate and organize information, as well as create and maintain knowledge in an infoglut.

A semantic recommendation system for cancer-related articles

2018 ◽  
Vol 02 (02) ◽  
pp. 1850013
Author(s):  
Charles C. N. Wang ◽  
Yun-Lung Chung ◽  
I-Seng Chang ◽  
Jeffrey J. P. Tsai
Keyword(s):  
Cancer Research ◽  
Text Mining ◽  
Recommendation System ◽  
Cancer Diagnostics ◽  
User Needs ◽  
Treatment And Prevention ◽  
Recommendation Accuracy ◽  
Number Of Publications ◽  
Enormous Number ◽  
Semantic Computing

There have been an enormous number of publications on cancer research. These unstructured cancer-related articles are of great value for cancer diagnostics, treatment, and prevention. The aim of this study is to introduce a recommendation system. It combines text mining (LDA) and semantic computing (GloVe) to understand the meaning of user needs and to increase the recommendation accuracy.

Information Retrieval (IR) and Extracting Associative Rules

2016 ◽  
pp. 713-732
Author(s):  
Asmae Dami ◽  
Mohamed Fakir ◽  
Belaid Bouikhalene
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Retrieval System ◽  
Information Retrieval System ◽  
User Needs ◽  
Retrieval Process ◽  
Research Themes ◽  
Document Database

This chapter is located in the intersection of two research themes, namely: Information Retrieval and Knowledge Discovery from texts (Text mining). The purpose of this paper is two-fold: first, it focuses on Information Retrieval (IR) whose purpose is to implement a set of models and systems for selecting a set of documents satisfying user needs in terms of information expressed as a query. An information retrieval system is composed mainly of two processes the representation and retrieval process. The process of representation is called indexing, which allows representation of documents and queries by descriptors, or indexes. These descriptors reflect the contents of documents. The retrieval process consists on the comparison between documents representations and query representation. The second aim of this paper is to discover the relationships between terms (keywords) descriptors of documents in a document database. The correlations (relationships) between terms are extracted by using a technique of the Text mining, mainly association rules.

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