Department Events
Text Mining Lab
This Course enables the students to
1 Acquaint students with text mining through Python.
2 Analyze data with different text mining methods.
Course | Learning outcome (at course level) | Learning and teaching strategies | Assessment Strategies | |
Course Code | Course Title | |||
25DBDA 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 |
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
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)
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
