Text Mining Lab

Paper Code: 
DBDA 512B
Credits: 
3
Periods/week: 
6
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 (COs).

Course Outcome (at course level)

 

Learning and teaching strategies

Assessment Strategies

On completion of this course, the students will:

CO261. Describe the big data concepts and applications in handling text data.

CO262.  Investigate the need for mining text data through various real-life scenarios.

CO263.Create and access datasets to implement mining.

 CO264.Perform preprocessing clustering and classification on text

CO265.Evaluate the performance of classification models.

 

 

Interactive Lectures, Discussion, Tutorials, reading assignments, Demonstrations, G-suite. Self-learning assignments, Effective questions, Simulation, Seminar presentation, giving tasks, Performing practical.

 

  • 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: 

E RESOURCES:

JOURNALS:

  • Text and Data Mining,
  • Elsevier

 

Academic Year: 
2022-23
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