Text Mining Lab

Paper Code: 
24DBDA512B
Credits: 
03
Periods/week: 
06
Max. Marks: 
100.00
Objective: 

This Course enables the students to

  1.  Acquaint students with text mining through Python.
  2.   Analyze data with different text mining methods.
Course Outcomes: 

Course

Learning outcome

(at course level)

Learning and teaching strategies

Assessment Strategies

Course Code

Course

Title

24DBDA 512 B

Text Mining Lab

(Practical)

 

 

CO313. Assess big data principles and their applications for text data management.

CO314.  Investigate the necessity of text data mining across diverse problems.

CO315. Formulate data mining challenges, access datasets, and implement solutions.

 CO316.Perform preprocessing clustering and classification on text.

CO317.Evaluate the performance of classification models.

CO318.Contribute effectively in course-specific interaction

Approach in teaching: Interactive Lectures, Discussion,

 

Demonstration,

 

Learning activities for the students:

 

Self-learning assignments, Practical questions

  • Assignment
  • Classroom activity
  • Multiple choice questions
  • Semester End Examination
  • Individual and group projects
 

 

This paper will be based on theory paper. Exercises given will be covering entire syllabi as follows.

  1. Big data applications
  2. Preparation of datasets for text mining
  3. Exercises related to text representation
  4. Exercises related to preprocess text data
  5. Exercises related to text clustering
  6. Exercises related to text classification
  7. Case Studies

 

ESSENTIAL READINGS: 

SUGGESTED TEXT BOOKS

  1. Big Data, Black Book, DT Editorial Services, DreamTech Press 2015
  2. Text Mining with Machine Learning. Principles and Techniques 1st Edition, CRC Press; 1st edition (November 11, 2019)

 

REFERENCES: 

SUGGESTED READINGS:

  1. Text Data Mining Springer; 1st ed. 2021 edition (May 23, 2021)

e RESOURCES

  1. NOC:Business Analytics & Text Mining Modeling Using Python, IIT Roorkee: https://nptel.ac.in/courses/110107129
  2. Datascience.com,textmining:https.//towardsdatascience.com/text-representation-for-data-science-and-text-mining-719ce81f3c84

JOURNALS

  1. Text and Data Mining, Elsevier: https://www.elsevier.com/open-science/research-data/text-and-data-mining

 

Academic Year: 
2024-25
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