SlideShare a Scribd company logo
1 of 2
Discussion
(Chapter 7): What are the common challenges with which
sentiment analysis deals? What are the most popular application
areas for sentiment analysis? Why?
Questions for Discussions:
1. Explain the relationship among data mining, text mining, and
sentiment analysis.
2. In your own words, define text mining, and discuss its most
popular applications.
3. What does it mean to induce structure into text-based data?
Discuss the alternative ways of inducing structure into them.
4. What is the role of NLP in text mining? Discuss the
capabilities and limitations of NLP in the context of text
mining.
Exercise:
Go to teradatauniversitynetwork.com and find the case study
named “eBay Analytics.” Read the case carefully and extend
your understanding of it by searching the Internet for additional
information, and answer the case questions.
Internet exercise:
Go to kdnuggets.com. Explore the sections on applications as
well as software. Find the names of at least three additional
packages for data mining and text mining.
Discussion (Chapter 7) What are the common challenges with which .docx

More Related Content

Similar to Discussion (Chapter 7) What are the common challenges with which .docx

SearchInFocus: Exploratory Study on Query Logs and Actionable Intelligence
SearchInFocus: Exploratory Study on Query Logs and Actionable Intelligence SearchInFocus: Exploratory Study on Query Logs and Actionable Intelligence
SearchInFocus: Exploratory Study on Query Logs and Actionable Intelligence Marina Santini
 
1. How do you describe the importance of data in analyticsC.docx
1. How do you describe the importance of data in analyticsC.docx1. How do you describe the importance of data in analyticsC.docx
1. How do you describe the importance of data in analyticsC.docxberthacarradice
 
Content Analyst - Conceptualizing LSI Based Text Analytics White Paper
Content Analyst - Conceptualizing LSI Based Text Analytics White PaperContent Analyst - Conceptualizing LSI Based Text Analytics White Paper
Content Analyst - Conceptualizing LSI Based Text Analytics White PaperJohn Felahi
 
Copyright © 2014 EMC Corporation. All rights reserved.Copy.docx
Copyright © 2014 EMC Corporation. All rights reserved.Copy.docxCopyright © 2014 EMC Corporation. All rights reserved.Copy.docx
Copyright © 2014 EMC Corporation. All rights reserved.Copy.docxmelvinjrobinson2199
 
Classification of News and Research Articles Using Text Pattern Mining
Classification of News and Research Articles Using Text Pattern MiningClassification of News and Research Articles Using Text Pattern Mining
Classification of News and Research Articles Using Text Pattern MiningIOSR Journals
 
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information Retrieval
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information RetrievalKeystone Summer School 2015: Mauro Dragoni, Ontologies For Information Retrieval
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information RetrievalMauro Dragoni
 
Discussion Create a discussion thread (with your name) and
Discussion Create a discussion thread (with your name) and Discussion Create a discussion thread (with your name) and
Discussion Create a discussion thread (with your name) and widdowsonerica
 
Information_Retrieval_Models_Nfaoui_El_Habib
Information_Retrieval_Models_Nfaoui_El_HabibInformation_Retrieval_Models_Nfaoui_El_Habib
Information_Retrieval_Models_Nfaoui_El_HabibEl Habib NFAOUI
 
Post 1What is text analytics How does it differ from text mini.docx
Post 1What is text analytics How does it differ from text mini.docxPost 1What is text analytics How does it differ from text mini.docx
Post 1What is text analytics How does it differ from text mini.docxstilliegeorgiana
 
Post 1What is text analytics How does it differ from text mini
Post 1What is text analytics How does it differ from text miniPost 1What is text analytics How does it differ from text mini
Post 1What is text analytics How does it differ from text minianhcrowley
 
Web_Mining_Overview_Nfaoui_El_Habib
Web_Mining_Overview_Nfaoui_El_HabibWeb_Mining_Overview_Nfaoui_El_Habib
Web_Mining_Overview_Nfaoui_El_HabibEl Habib NFAOUI
 
Unit 1 Introduction to Artificial Intelligence.pptx
Unit 1 Introduction to Artificial Intelligence.pptxUnit 1 Introduction to Artificial Intelligence.pptx
Unit 1 Introduction to Artificial Intelligence.pptxDr.M.Karthika parthasarathy
 
1. introduction to data science —
1. introduction to data science —1. introduction to data science —
1. introduction to data science —swethaT16
 
Text Classification.pptx
Text Classification.pptxText Classification.pptx
Text Classification.pptxhezamgawbah
 
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative...
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative...Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative...
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative...CITE
 
Text analysis-semantic-search
Text analysis-semantic-searchText analysis-semantic-search
Text analysis-semantic-searchDiana Maynard
 
Framework for Product Recommandation for Review Dataset
Framework for Product Recommandation for Review DatasetFramework for Product Recommandation for Review Dataset
Framework for Product Recommandation for Review Datasetrahulmonikasharma
 

Similar to Discussion (Chapter 7) What are the common challenges with which .docx (20)

SearchInFocus: Exploratory Study on Query Logs and Actionable Intelligence
SearchInFocus: Exploratory Study on Query Logs and Actionable Intelligence SearchInFocus: Exploratory Study on Query Logs and Actionable Intelligence
SearchInFocus: Exploratory Study on Query Logs and Actionable Intelligence
 
Anu paper(IJARCCE)
Anu paper(IJARCCE)Anu paper(IJARCCE)
Anu paper(IJARCCE)
 
1. How do you describe the importance of data in analyticsC.docx
1. How do you describe the importance of data in analyticsC.docx1. How do you describe the importance of data in analyticsC.docx
1. How do you describe the importance of data in analyticsC.docx
 
Content Analyst - Conceptualizing LSI Based Text Analytics White Paper
Content Analyst - Conceptualizing LSI Based Text Analytics White PaperContent Analyst - Conceptualizing LSI Based Text Analytics White Paper
Content Analyst - Conceptualizing LSI Based Text Analytics White Paper
 
Copyright © 2014 EMC Corporation. All rights reserved.Copy.docx
Copyright © 2014 EMC Corporation. All rights reserved.Copy.docxCopyright © 2014 EMC Corporation. All rights reserved.Copy.docx
Copyright © 2014 EMC Corporation. All rights reserved.Copy.docx
 
Classification of News and Research Articles Using Text Pattern Mining
Classification of News and Research Articles Using Text Pattern MiningClassification of News and Research Articles Using Text Pattern Mining
Classification of News and Research Articles Using Text Pattern Mining
 
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information Retrieval
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information RetrievalKeystone Summer School 2015: Mauro Dragoni, Ontologies For Information Retrieval
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information Retrieval
 
Discussion Create a discussion thread (with your name) and
Discussion Create a discussion thread (with your name) and Discussion Create a discussion thread (with your name) and
Discussion Create a discussion thread (with your name) and
 
Information_Retrieval_Models_Nfaoui_El_Habib
Information_Retrieval_Models_Nfaoui_El_HabibInformation_Retrieval_Models_Nfaoui_El_Habib
Information_Retrieval_Models_Nfaoui_El_Habib
 
Post 1What is text analytics How does it differ from text mini.docx
Post 1What is text analytics How does it differ from text mini.docxPost 1What is text analytics How does it differ from text mini.docx
Post 1What is text analytics How does it differ from text mini.docx
 
Post 1What is text analytics How does it differ from text mini
Post 1What is text analytics How does it differ from text miniPost 1What is text analytics How does it differ from text mini
Post 1What is text analytics How does it differ from text mini
 
Web_Mining_Overview_Nfaoui_El_Habib
Web_Mining_Overview_Nfaoui_El_HabibWeb_Mining_Overview_Nfaoui_El_Habib
Web_Mining_Overview_Nfaoui_El_Habib
 
Unit 1 Introduction to Artificial Intelligence.pptx
Unit 1 Introduction to Artificial Intelligence.pptxUnit 1 Introduction to Artificial Intelligence.pptx
Unit 1 Introduction to Artificial Intelligence.pptx
 
1. introduction to data science —
1. introduction to data science —1. introduction to data science —
1. introduction to data science —
 
Text Classification.pptx
Text Classification.pptxText Classification.pptx
Text Classification.pptx
 
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.Padmapriya
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.Padmapriya[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.Padmapriya
[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.Padmapriya
 
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative...
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative...Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative...
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative...
 
The Three Core Topic Types
The Three Core Topic TypesThe Three Core Topic Types
The Three Core Topic Types
 
Text analysis-semantic-search
Text analysis-semantic-searchText analysis-semantic-search
Text analysis-semantic-search
 
Framework for Product Recommandation for Review Dataset
Framework for Product Recommandation for Review DatasetFramework for Product Recommandation for Review Dataset
Framework for Product Recommandation for Review Dataset
 

More from mecklenburgstrelitzh

Discussion - Week 3Elements of the Craft of WritingThe narra.docx
Discussion - Week 3Elements of the Craft of WritingThe narra.docxDiscussion - Week 3Elements of the Craft of WritingThe narra.docx
Discussion - Week 3Elements of the Craft of WritingThe narra.docxmecklenburgstrelitzh
 
Discussion - Microbial ClassificationGive names of bacteria in.docx
Discussion - Microbial ClassificationGive names of bacteria in.docxDiscussion - Microbial ClassificationGive names of bacteria in.docx
Discussion - Microbial ClassificationGive names of bacteria in.docxmecklenburgstrelitzh
 
Discussion (Chapter 7) What are the common challenges with which se.docx
Discussion (Chapter 7) What are the common challenges with which se.docxDiscussion (Chapter 7) What are the common challenges with which se.docx
Discussion (Chapter 7) What are the common challenges with which se.docxmecklenburgstrelitzh
 
Discussion - Big Data Visualization toolsSeveral Big Data Visu.docx
Discussion - Big Data Visualization toolsSeveral Big Data Visu.docxDiscussion - Big Data Visualization toolsSeveral Big Data Visu.docx
Discussion - Big Data Visualization toolsSeveral Big Data Visu.docxmecklenburgstrelitzh
 
Discussion - 1 Pick 2 different department team members and descri.docx
Discussion - 1  Pick 2 different department team members and descri.docxDiscussion - 1  Pick 2 different department team members and descri.docx
Discussion - 1 Pick 2 different department team members and descri.docxmecklenburgstrelitzh
 
Discussion (Chapter 7) What are the common challenges with whic.docx
Discussion (Chapter 7) What are the common challenges with whic.docxDiscussion (Chapter 7) What are the common challenges with whic.docx
Discussion (Chapter 7) What are the common challenges with whic.docxmecklenburgstrelitzh
 
Discussion (Chapter 6) List and briefly describe the nine-step .docx
Discussion (Chapter 6) List and briefly describe the nine-step .docxDiscussion (Chapter 6) List and briefly describe the nine-step .docx
Discussion (Chapter 6) List and briefly describe the nine-step .docxmecklenburgstrelitzh
 
Discussion (Chapter 5) What is the relationship between Naïve Bayes.docx
Discussion (Chapter 5) What is the relationship between Naïve Bayes.docxDiscussion (Chapter 5) What is the relationship between Naïve Bayes.docx
Discussion (Chapter 5) What is the relationship between Naïve Bayes.docxmecklenburgstrelitzh
 
Discussion (Chapter 4) What are the privacy issues with data mini.docx
Discussion (Chapter 4) What are the privacy issues with data mini.docxDiscussion (Chapter 4) What are the privacy issues with data mini.docx
Discussion (Chapter 4) What are the privacy issues with data mini.docxmecklenburgstrelitzh
 
Discussion (Chapter 3) Why are the originalraw data not readily us.docx
Discussion (Chapter 3) Why are the originalraw data not readily us.docxDiscussion (Chapter 3) Why are the originalraw data not readily us.docx
Discussion (Chapter 3) Why are the originalraw data not readily us.docxmecklenburgstrelitzh
 
Discussion (Chapter 5) What is the relationship between Naïve B.docx
Discussion (Chapter 5) What is the relationship between Naïve B.docxDiscussion (Chapter 5) What is the relationship between Naïve B.docx
Discussion (Chapter 5) What is the relationship between Naïve B.docxmecklenburgstrelitzh
 
Discussion (Chapter 10 in the textbook or see the ppt) For ea.docx
Discussion (Chapter 10 in the textbook  or see the ppt) For ea.docxDiscussion (Chapter 10 in the textbook  or see the ppt) For ea.docx
Discussion (Chapter 10 in the textbook or see the ppt) For ea.docxmecklenburgstrelitzh
 
Discussion (Chapter 1) Compare and contrast predictive analytics wi.docx
Discussion (Chapter 1) Compare and contrast predictive analytics wi.docxDiscussion (Chapter 1) Compare and contrast predictive analytics wi.docx
Discussion (Chapter 1) Compare and contrast predictive analytics wi.docxmecklenburgstrelitzh
 
Discussion (400 words discussion + 150 words student response)Co.docx
Discussion (400 words discussion + 150 words student response)Co.docxDiscussion (400 words discussion + 150 words student response)Co.docx
Discussion (400 words discussion + 150 words student response)Co.docxmecklenburgstrelitzh
 
Discussion (150-200 words) Why do you think so much emphasis is pla.docx
Discussion (150-200 words) Why do you think so much emphasis is pla.docxDiscussion (150-200 words) Why do you think so much emphasis is pla.docx
Discussion (150-200 words) Why do you think so much emphasis is pla.docxmecklenburgstrelitzh
 
discussion (11)explain the concept of information stores as th.docx
discussion (11)explain the concept of information stores as th.docxdiscussion (11)explain the concept of information stores as th.docx
discussion (11)explain the concept of information stores as th.docxmecklenburgstrelitzh
 
Discussion #5 How progressive was the Progressive EraThe Progres.docx
Discussion #5 How progressive was the Progressive EraThe Progres.docxDiscussion #5 How progressive was the Progressive EraThe Progres.docx
Discussion #5 How progressive was the Progressive EraThe Progres.docxmecklenburgstrelitzh
 
Discussion #4, Continued Work on VygotskyA. Why is it important .docx
Discussion #4, Continued Work on VygotskyA. Why is it important .docxDiscussion #4, Continued Work on VygotskyA. Why is it important .docx
Discussion #4, Continued Work on VygotskyA. Why is it important .docxmecklenburgstrelitzh
 
Discussion #4 What are the most common metrics that make for an.docx
Discussion #4 What are the most common metrics that make for an.docxDiscussion #4 What are the most common metrics that make for an.docx
Discussion #4 What are the most common metrics that make for an.docxmecklenburgstrelitzh
 
Discussion #3What is your perception of the community health.docx
Discussion #3What is your perception of the community health.docxDiscussion #3What is your perception of the community health.docx
Discussion #3What is your perception of the community health.docxmecklenburgstrelitzh
 

More from mecklenburgstrelitzh (20)

Discussion - Week 3Elements of the Craft of WritingThe narra.docx
Discussion - Week 3Elements of the Craft of WritingThe narra.docxDiscussion - Week 3Elements of the Craft of WritingThe narra.docx
Discussion - Week 3Elements of the Craft of WritingThe narra.docx
 
Discussion - Microbial ClassificationGive names of bacteria in.docx
Discussion - Microbial ClassificationGive names of bacteria in.docxDiscussion - Microbial ClassificationGive names of bacteria in.docx
Discussion - Microbial ClassificationGive names of bacteria in.docx
 
Discussion (Chapter 7) What are the common challenges with which se.docx
Discussion (Chapter 7) What are the common challenges with which se.docxDiscussion (Chapter 7) What are the common challenges with which se.docx
Discussion (Chapter 7) What are the common challenges with which se.docx
 
Discussion - Big Data Visualization toolsSeveral Big Data Visu.docx
Discussion - Big Data Visualization toolsSeveral Big Data Visu.docxDiscussion - Big Data Visualization toolsSeveral Big Data Visu.docx
Discussion - Big Data Visualization toolsSeveral Big Data Visu.docx
 
Discussion - 1 Pick 2 different department team members and descri.docx
Discussion - 1  Pick 2 different department team members and descri.docxDiscussion - 1  Pick 2 different department team members and descri.docx
Discussion - 1 Pick 2 different department team members and descri.docx
 
Discussion (Chapter 7) What are the common challenges with whic.docx
Discussion (Chapter 7) What are the common challenges with whic.docxDiscussion (Chapter 7) What are the common challenges with whic.docx
Discussion (Chapter 7) What are the common challenges with whic.docx
 
Discussion (Chapter 6) List and briefly describe the nine-step .docx
Discussion (Chapter 6) List and briefly describe the nine-step .docxDiscussion (Chapter 6) List and briefly describe the nine-step .docx
Discussion (Chapter 6) List and briefly describe the nine-step .docx
 
Discussion (Chapter 5) What is the relationship between Naïve Bayes.docx
Discussion (Chapter 5) What is the relationship between Naïve Bayes.docxDiscussion (Chapter 5) What is the relationship between Naïve Bayes.docx
Discussion (Chapter 5) What is the relationship between Naïve Bayes.docx
 
Discussion (Chapter 4) What are the privacy issues with data mini.docx
Discussion (Chapter 4) What are the privacy issues with data mini.docxDiscussion (Chapter 4) What are the privacy issues with data mini.docx
Discussion (Chapter 4) What are the privacy issues with data mini.docx
 
Discussion (Chapter 3) Why are the originalraw data not readily us.docx
Discussion (Chapter 3) Why are the originalraw data not readily us.docxDiscussion (Chapter 3) Why are the originalraw data not readily us.docx
Discussion (Chapter 3) Why are the originalraw data not readily us.docx
 
Discussion (Chapter 5) What is the relationship between Naïve B.docx
Discussion (Chapter 5) What is the relationship between Naïve B.docxDiscussion (Chapter 5) What is the relationship between Naïve B.docx
Discussion (Chapter 5) What is the relationship between Naïve B.docx
 
Discussion (Chapter 10 in the textbook or see the ppt) For ea.docx
Discussion (Chapter 10 in the textbook  or see the ppt) For ea.docxDiscussion (Chapter 10 in the textbook  or see the ppt) For ea.docx
Discussion (Chapter 10 in the textbook or see the ppt) For ea.docx
 
Discussion (Chapter 1) Compare and contrast predictive analytics wi.docx
Discussion (Chapter 1) Compare and contrast predictive analytics wi.docxDiscussion (Chapter 1) Compare and contrast predictive analytics wi.docx
Discussion (Chapter 1) Compare and contrast predictive analytics wi.docx
 
Discussion (400 words discussion + 150 words student response)Co.docx
Discussion (400 words discussion + 150 words student response)Co.docxDiscussion (400 words discussion + 150 words student response)Co.docx
Discussion (400 words discussion + 150 words student response)Co.docx
 
Discussion (150-200 words) Why do you think so much emphasis is pla.docx
Discussion (150-200 words) Why do you think so much emphasis is pla.docxDiscussion (150-200 words) Why do you think so much emphasis is pla.docx
Discussion (150-200 words) Why do you think so much emphasis is pla.docx
 
discussion (11)explain the concept of information stores as th.docx
discussion (11)explain the concept of information stores as th.docxdiscussion (11)explain the concept of information stores as th.docx
discussion (11)explain the concept of information stores as th.docx
 
Discussion #5 How progressive was the Progressive EraThe Progres.docx
Discussion #5 How progressive was the Progressive EraThe Progres.docxDiscussion #5 How progressive was the Progressive EraThe Progres.docx
Discussion #5 How progressive was the Progressive EraThe Progres.docx
 
Discussion #4, Continued Work on VygotskyA. Why is it important .docx
Discussion #4, Continued Work on VygotskyA. Why is it important .docxDiscussion #4, Continued Work on VygotskyA. Why is it important .docx
Discussion #4, Continued Work on VygotskyA. Why is it important .docx
 
Discussion #4 What are the most common metrics that make for an.docx
Discussion #4 What are the most common metrics that make for an.docxDiscussion #4 What are the most common metrics that make for an.docx
Discussion #4 What are the most common metrics that make for an.docx
 
Discussion #3What is your perception of the community health.docx
Discussion #3What is your perception of the community health.docxDiscussion #3What is your perception of the community health.docx
Discussion #3What is your perception of the community health.docx
 

Recently uploaded

Crayon Activity Handout For the Crayon A
Crayon Activity Handout For the Crayon ACrayon Activity Handout For the Crayon A
Crayon Activity Handout For the Crayon AUnboundStockton
 
Meghan Sutherland In Media Res Media Component
Meghan Sutherland In Media Res Media ComponentMeghan Sutherland In Media Res Media Component
Meghan Sutherland In Media Res Media ComponentInMediaRes1
 
Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdf
Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdfEnzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdf
Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdfSumit Tiwari
 
CARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxCARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxGaneshChakor2
 
Introduction to ArtificiaI Intelligence in Higher Education
Introduction to ArtificiaI Intelligence in Higher EducationIntroduction to ArtificiaI Intelligence in Higher Education
Introduction to ArtificiaI Intelligence in Higher Educationpboyjonauth
 
Types of Journalistic Writing Grade 8.pptx
Types of Journalistic Writing Grade 8.pptxTypes of Journalistic Writing Grade 8.pptx
Types of Journalistic Writing Grade 8.pptxEyham Joco
 
Framing an Appropriate Research Question 6b9b26d93da94caf993c038d9efcdedb.pdf
Framing an Appropriate Research Question 6b9b26d93da94caf993c038d9efcdedb.pdfFraming an Appropriate Research Question 6b9b26d93da94caf993c038d9efcdedb.pdf
Framing an Appropriate Research Question 6b9b26d93da94caf993c038d9efcdedb.pdfUjwalaBharambe
 
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...Marc Dusseiller Dusjagr
 
Roles & Responsibilities in Pharmacovigilance
Roles & Responsibilities in PharmacovigilanceRoles & Responsibilities in Pharmacovigilance
Roles & Responsibilities in PharmacovigilanceSamikshaHamane
 
भारत-रोम व्यापार.pptx, Indo-Roman Trade,
भारत-रोम व्यापार.pptx, Indo-Roman Trade,भारत-रोम व्यापार.pptx, Indo-Roman Trade,
भारत-रोम व्यापार.pptx, Indo-Roman Trade,Virag Sontakke
 
Hierarchy of management that covers different levels of management
Hierarchy of management that covers different levels of managementHierarchy of management that covers different levels of management
Hierarchy of management that covers different levels of managementmkooblal
 
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPTECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPTiammrhaywood
 
DATA STRUCTURE AND ALGORITHM for beginners
DATA STRUCTURE AND ALGORITHM for beginnersDATA STRUCTURE AND ALGORITHM for beginners
DATA STRUCTURE AND ALGORITHM for beginnersSabitha Banu
 
Solving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxSolving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxOH TEIK BIN
 
Incoming and Outgoing Shipments in 1 STEP Using Odoo 17
Incoming and Outgoing Shipments in 1 STEP Using Odoo 17Incoming and Outgoing Shipments in 1 STEP Using Odoo 17
Incoming and Outgoing Shipments in 1 STEP Using Odoo 17Celine George
 
EPANDING THE CONTENT OF AN OUTLINE using notes.pptx
EPANDING THE CONTENT OF AN OUTLINE using notes.pptxEPANDING THE CONTENT OF AN OUTLINE using notes.pptx
EPANDING THE CONTENT OF AN OUTLINE using notes.pptxRaymartEstabillo3
 
Final demo Grade 9 for demo Plan dessert.pptx
Final demo Grade 9 for demo Plan dessert.pptxFinal demo Grade 9 for demo Plan dessert.pptx
Final demo Grade 9 for demo Plan dessert.pptxAvyJaneVismanos
 
Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17Celine George
 

Recently uploaded (20)

Crayon Activity Handout For the Crayon A
Crayon Activity Handout For the Crayon ACrayon Activity Handout For the Crayon A
Crayon Activity Handout For the Crayon A
 
Meghan Sutherland In Media Res Media Component
Meghan Sutherland In Media Res Media ComponentMeghan Sutherland In Media Res Media Component
Meghan Sutherland In Media Res Media Component
 
Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdf
Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdfEnzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdf
Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdf
 
CARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxCARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptx
 
Introduction to ArtificiaI Intelligence in Higher Education
Introduction to ArtificiaI Intelligence in Higher EducationIntroduction to ArtificiaI Intelligence in Higher Education
Introduction to ArtificiaI Intelligence in Higher Education
 
OS-operating systems- ch04 (Threads) ...
OS-operating systems- ch04 (Threads) ...OS-operating systems- ch04 (Threads) ...
OS-operating systems- ch04 (Threads) ...
 
9953330565 Low Rate Call Girls In Rohini Delhi NCR
9953330565 Low Rate Call Girls In Rohini  Delhi NCR9953330565 Low Rate Call Girls In Rohini  Delhi NCR
9953330565 Low Rate Call Girls In Rohini Delhi NCR
 
Types of Journalistic Writing Grade 8.pptx
Types of Journalistic Writing Grade 8.pptxTypes of Journalistic Writing Grade 8.pptx
Types of Journalistic Writing Grade 8.pptx
 
Framing an Appropriate Research Question 6b9b26d93da94caf993c038d9efcdedb.pdf
Framing an Appropriate Research Question 6b9b26d93da94caf993c038d9efcdedb.pdfFraming an Appropriate Research Question 6b9b26d93da94caf993c038d9efcdedb.pdf
Framing an Appropriate Research Question 6b9b26d93da94caf993c038d9efcdedb.pdf
 
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
 
Roles & Responsibilities in Pharmacovigilance
Roles & Responsibilities in PharmacovigilanceRoles & Responsibilities in Pharmacovigilance
Roles & Responsibilities in Pharmacovigilance
 
भारत-रोम व्यापार.pptx, Indo-Roman Trade,
भारत-रोम व्यापार.pptx, Indo-Roman Trade,भारत-रोम व्यापार.pptx, Indo-Roman Trade,
भारत-रोम व्यापार.pptx, Indo-Roman Trade,
 
Hierarchy of management that covers different levels of management
Hierarchy of management that covers different levels of managementHierarchy of management that covers different levels of management
Hierarchy of management that covers different levels of management
 
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPTECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
ECONOMIC CONTEXT - LONG FORM TV DRAMA - PPT
 
DATA STRUCTURE AND ALGORITHM for beginners
DATA STRUCTURE AND ALGORITHM for beginnersDATA STRUCTURE AND ALGORITHM for beginners
DATA STRUCTURE AND ALGORITHM for beginners
 
Solving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxSolving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptx
 
Incoming and Outgoing Shipments in 1 STEP Using Odoo 17
Incoming and Outgoing Shipments in 1 STEP Using Odoo 17Incoming and Outgoing Shipments in 1 STEP Using Odoo 17
Incoming and Outgoing Shipments in 1 STEP Using Odoo 17
 
EPANDING THE CONTENT OF AN OUTLINE using notes.pptx
EPANDING THE CONTENT OF AN OUTLINE using notes.pptxEPANDING THE CONTENT OF AN OUTLINE using notes.pptx
EPANDING THE CONTENT OF AN OUTLINE using notes.pptx
 
Final demo Grade 9 for demo Plan dessert.pptx
Final demo Grade 9 for demo Plan dessert.pptxFinal demo Grade 9 for demo Plan dessert.pptx
Final demo Grade 9 for demo Plan dessert.pptx
 
Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17
 

Discussion (Chapter 7) What are the common challenges with which .docx

  • 1. Discussion (Chapter 7): What are the common challenges with which sentiment analysis deals? What are the most popular application areas for sentiment analysis? Why? Questions for Discussions: 1. Explain the relationship among data mining, text mining, and sentiment analysis. 2. In your own words, define text mining, and discuss its most popular applications. 3. What does it mean to induce structure into text-based data? Discuss the alternative ways of inducing structure into them. 4. What is the role of NLP in text mining? Discuss the capabilities and limitations of NLP in the context of text mining. Exercise: Go to teradatauniversitynetwork.com and find the case study named “eBay Analytics.” Read the case carefully and extend your understanding of it by searching the Internet for additional information, and answer the case questions. Internet exercise: Go to kdnuggets.com. Explore the sections on applications as well as software. Find the names of at least three additional packages for data mining and text mining.