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Published: May 31, 2024
Chapter 7 of the Assigned Reading offers a very detailed description of text - mining one of its more common uses and sentiment analysis, all relevant to market analytics as well as decision support tools. As such, sentiment analysis would be a text mining derived, and otherwise, text mining becomes basically a data mining derivative. As textual data within organized databases rises throughout size or quantity instead of data, it is vital to recognize many methods that used obtain meaningful information from such a vast number of unstructured- data. Chapter 7 addresses text mining and provides readers with an appreciation of the need for text-mining. Text mining has been the extraction of knowledge mostly from non-structured (mainly text-based) sources of data (Avram, Gligor & Avram, 2020). Data mining is among the largest rising divisions of a BI- business intelligence sector or even industry, provided that a significant amount of information has been in text form. In particular, this chapter gives strong differences between text mining & text analytics, and data mining. The numerous main applications of text mining were given as well.
Text mining technologies span nearly every field of industry and government, like advertising, banking, healthcare, pharmacy as well as homeland protection or even security. The method for carrying out such a text- mining plan has also been clarified throughout this chapter. Text mining utilizes natural language procedure to organize the set of text and also utilizes data mining techniques including such sorting, association & clustering as well as sequence discovery for extract knowledge from all of this. - A standardized approach closes to a CRISP-DM approach of such data - mining includes the efficient implementation of text- mining. Chapter 7 introduces and describes the concept of sentiment analysis (Avram, Gligor & Avram, 2020). As an area of research, sentiment analysis becomes strongly connected to natural language, quantitative linguistics, and text mining. It could be used to optimize search outcomes created by search engines. Sentiment research aimed to address the question of -
What do people are feeling about a certain topic? by utilizing a number with automated methods to dig through the thoughts of many (Avram, Gligor & Avram, 2020). This chapter enhances awareness regarding popular sentiment techniques as well as allows readers to understand the popular techniques of sentiment methods or analysis.
Chapter 8 is more about Web mining as well as its fields of use. As they understand, one of the fastest developing BI and (BA)- business analytics innovations become Web mining. This chapter discusses search engines as well as Web analytics, social analytics and the supporting tools, algorithms, as well as developments within the umbrella for Web mining. This chapter presents and identifies Web mining as well as describes its taxonomy as well as its fields of use. Web mining could be described as exploration & analysis for web, and generally web-based resources that provide useful information and interesting. Provides a difference between Web structure mining as well as Web mining material. Describes the concept of such Internet search engine internal parts.
Chapter 8 explains the theory of a Web analytics sophistication model, as well as its usage cases, also offers details on (SEO) - search engine optimization. SEO is the deliberate practice that influences the exposure of a normal search engine outcomes of an e-commerce site or even a website in such a search engine. A research framework is really a formal representation of such a company practice's essential dimensions as well as the levels of competency. Social networks as well as social analytics and otherwise their practical uses are explained in this chapter. This chapter allows the readers to consider & utilize the idea of social network analytics for deeper engagement with consumers. The tracking, review, evaluation, and perception of digital relationships & interactions among individuals, subjects, thoughts, & content are social analytics. Analytics of social networking relates to the comprehensive & scientific forms of accessing the large amount of content generated through web-based or dependent social media outlets, instruments, and strategies to boost the efficiency of even an organization.
Chapter 9 of the Specified Reading with Title “Model-Based Decision Making· Optimization and Multi-Criteria Systems” Identify selected approaches used throughout prescriptive analytics. The aim of this chapter isn't really necessary for all the aspects of modelling as well as analysis to be mastered. Instead, the material appears geared towards gaining familiarity with the significant principles as they apply to DSS as well as their application in decision-making. This chapter presents the core principles of modelling analytical actions as well as explains how prescriptive models deal with data and also the consumer. Within DSS, models contribute significantly if they're used to identify actual decision-making scenarios. Several kinds of models exist. Models may be static (for example, a single scenario snapshot) or otherwise dynamic (e.g multiperiod). Research shall be performed on the basis of presumed certainty (which would be more desirable), risk or even uncertainty (which will be the least desirable).
Furthermore, Chapter 9 explains how spreadsheets could be used for modelling as well as solution analytics. There are several capabilities for spreadsheets, such as what analysis, target finding, programming, optimization, control of databases and simulation. Decision tables & decision trees could model basic decision-making issues and solve them. This chapter explains how a linear programming model can be organized and also how different objectives can be treated. Describe what is implied by sensitivity analysis, what-if evaluation, and otherwise, LP goal-searching has been the most popular mathematical programming tool. Within operational constraints, it seeks to find an effective distribution of available capital. The goal feature, the decision factors, as well as the limitations are also the key components of such an LP model. The key problems in multi-criteria decision-making are illustrated in this portion. Decision-making challenges involving several parameters are difficult, but it's not impossible to overcome.
Chapter 10 of an Assigned Reading proceeds to discuss certain specific topics relevant to a model base, one of the core elements of DSS- decision support systems. This chapter discusses the fundamental principles of, and where to use, simulation & heuristics. This chapter helps us recognize how search techniques have been used to overcome such types for decision support and also to understand the principles behind the genetic- algorithms and their implementations. The simulation would be a widely used technique to DSS requiring experimenting with such a model which illustrates the actual circumstance of decision- making. The simulation could manage conditions that are more complicated than optimization, and that does not ensure an optimum solution. Many various ways of simulation exist. systems dynamics modelling, those that are essential to DSS involve discrete event simulation, system dynamics modelling, Monte Carlo simulation, and agent- based simulation. VIS/VIM helps decision-makers to engage explicitly with such a model as well as displays results in such a way that is readily understood. The distinctions between algorithms, blinded search, as well as heuristics are described in this chapter, and the principles and implementations of various simulation styles are explained. In particular, this segment summarizes what's been described by Monte Carlo, agent-based modelling, system dynamics, as well as a discrete simulation for events & describes the main model management problems.
Text mining (also recognized as text analysis) is also an automated method of translating unstructured text through simple as well as meaningful information. To remove organizations & sort texts through topic, sentiment, purpose, urgency, and much more, it could be used. Equipped for NLP- Natural Language Processing, text mining techniques are being used to evaluate all forms in the text, including survey comments as well as communications to tweets as well as product feedback, helping companies obtain insight & make data-based decisions. There are all the top 6 providers of text analytics to always be aware of:
They would analyze the visualization platforms and key NLP (natural language processing) engines to analyze each supplier's offers. They're also going to look at certain feedback from customers and equate certain feedback with organizational communications. As seen in Figure 1, Text Mining Methods could be divided into three groups.
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Proprietary Text Mining Tools: Such tools were company-owned proprietary text-mining techniques. This is necessary to purchase certain devices for using them. While demo or trial models are free of cost and have restricted features, they are usable.
Open-Source Text Mining Tools: Such tools, as well as the source code, is accessible at no cost and could also lead to growth.
Online Text Mining Tools: They may operate these resources from within the websites themselves. There is only a web browser needed. In specific, such devices are basic and have minimal functionality.
As the importance of such text-mining has been understood by many more companies the number of digital applications provided by technology companies & non-profit organizations is also growing. The follows are among the common tools for text -mining, which they identify as proprietary software applications and free software applications.
Commercial Software Tools Many of the most common software tools used during text mining were listed below. Notice it on the web sites, several businesses provide demonstration models of their products.
Free Software Applications Free software tools, many of which are open access, are accessible from a range of non-profit companies:
Web mining has become a computer program that utilizes data mining strategies to find or uncover trends from massive data sets. There are three fields of web mining: web usage mining, web content mining as well as web structure mining. Any of the useful methods for web mining are below.
R: R is a free language / environment of statistical computing & graphics. Scripting languages such as Ruby, Python, Perl and so on have rendered it available.
Octoparse: It is an easy yet strong method for web data mining which simplifies the processing of web data.
ProWebScraper: ProWebScraper is also an amazing mining & web scraping application for web content.
Weka: Weka is a set of algorithms which can be used for different tasks relevant to data mining. It requires different data classification tools, planning, clustering, regression, visualization, and much more.
Majestic: Majestic is also an extremely efficient mining tool for web structures which is used in business analytics. It offers web-based link-investigation, Search Engine Optimization techniques, and much more.
SimilarWeb: SimilarWeb is also another web-based mining & market intelligence platform. It empowers companies to make better decisions by using its online mining capability.
Tableau: Tableau provides a family of BI-focused digital data visualization goods.
Scrapy: Scrapy is also an open-source platform for website data analysis. This is written in Python and the rules for extracting site data could be written.
Web as well as web use has been continuing to grow, therefore the ability to examine web data and derive all sorts of useful knowledge from it is also growing. A web-enabled digital business may be assisted through web mining strategies to optimize marketing, customer service and sales activities. Web mining implementation is related to the rapid development of the (W.W.W)- world wide web; throughout the field of web science, web-mining is a very popular and common subject. For E-Commerce & E-Service website, web mining often plays a key role in recognizing the use of their websites and services and offering improved service for both consumers and users. E-Learning, e-government, digital libraries, mobile trade, surveillance and crime investigation, as well as electronic enterprise are only a few applications.
Models as well as simulations are just like databases if used as analytical as well as decision support resources that adjust automatically with reaction to relationships among new and old information. Although a database would be a way to organize, preserve and scan for records, through one moment to the next, a simulation is really a versatile method for rearranging, mixing, modifying and testing new data combinations. This makes it a theoretically invaluable instrument focused on historical and current incidents as well as circumstances to anticipate possible terrorist acts. The technical simulation group has not based its energies or creativity heavily on even a full analysis of a risk of terrorism. As a consequence, models which capture all components of terrorism and search for warning signals of potential acts are required (Palanisamy & Liu, 2018). There are combat templates which allow Special Forces can rehearse attacks upon terrorist strongholds. Models for intelligence become accessible that describe the physical & logical interactions between participants of such a terrorist cell. What's really lacking, though, is a digital paradigm that combines the threat's military, fiscal, political, protection and legal aspects.
A category of machine learning to describe and solve complex problems is genetic algorithms. For a wide range of applications, they include a collection of effective, domain- independent search heuristics, such as the following:
A genetic algorithm interprets information that can help it to reject as well as collect strong solutions that are inferior, and thereby learns about its universe. For parallelization, genetic algorithms to are appropriate. Since the kernels of the evolutionary algorithm are fairly easy, it isn't hard to write programming codes to execute them. Software packages are required for improved performance (Palanisamy & Liu, 2018). In particular, online demonstrations are offered in a range of commercial bundles. Microsoft Solver as well as XpertRule Genisys, an ES shell with such an integrated genetic algorithm, provide representative commercial sets. To handle complicated optimization issues in financing, scheduling, development, and so on, it utilizes a genetic algorithm.
This paper offered me a lot of understandings about text mining, web mining, models, social analytics, optimization principles, simulation, sentiment analysis and heuristics. The semi-automated method of extracting trends (helpful information & knowledge) as well from vast numbers of unstructured data sources is text mining (as well recognized as text data mining or even knowledge exploration within textual databases). Data mining was its mechanism through which valid, new, potentially valuable and essentially understandable trends were found in data contained in hierarchical databases, where data is ordered through categorical, ordinal or even dependent variable in documents (Palanisamy & Liu, 2018). Text mining is the same as data- mining because it has the same intent and otherwise utilizes the same methods, except with more text mining, a series of unstructured (or even less structured) data files including such PDF files, Word documents, text extracts, XML files, etc. is the input to an operation. Sentiment assessment is a technique that utilizes vast quantities with textual sources of data to identify favorable as well as unfavorable views regarding specific products & services. While database mining (or even web data mining) seems to be the method of discovering as well from web data inherent relationships (i.e., important and valuable information) conveyed throughout the form of textual specifics, connections or user information.
Social analytics involves mining the textual data derived through social networking (e.g. emotion analysis, natural language) and the assessment through publicly developed networks (e.g. influencer recognition, sampling, prediction) to obtain insight about current and new clients' present and future habits, and into the perceptions and dislikes of a company's goods and interests. In certain DSS models, modelling would be a central aspect and a prerequisite in such a model-based DSS. There are several model groups, but there are also many specific methods to solve each one. A popular modelling technique is a simulation, and there are many others. It could save millions of such dollars or otherwise produce thousands of dollars in sales by adapting models to real-world scenarios. Heuristics really are the informal, judgmental awareness that constitutes the laws of good decision throughout the field of such a field of operation (Palanisamy & Liu, 2018). They lead the problem-solving method via domain knowledge. The method of utilizing heuristics throughout problem-solving becomes heuristic programming. Genetic algorithms (GA) are part of the international search techniques used by conventional optimization strategies to find possible answers to optimization-type problems which are too difficult to solve. Simulation is reality's presence. Simulation is a methodology in MSS for performing experiments (for example, what-if analyses) with a machine on even a management system model.
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