Subject » BMEVITMM277
Media and Text Mining
Média- és szövegbányászat
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
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| Subject name (Hungarian, English) |
Média- és szövegbányászat
Media and Text Mining
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| Subject code | BMEVITMM277 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
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| Assessment type | vizsga | ||||||||||||
| Credits | 6 | ||||||||||||
| Subject coordinator |
Szűcs Gábor, PhD
position: adjunktus
contact:
szucs.gabor@vik.bme.hu
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| Responsible department |
Távközlési és Mesterséges Intelligencia Tanszék
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| Faculty | Villamosmérnöki és Informatikai Kar | ||||||||||||
| Subject website | — | ||||||||||||
| Primary curriculum type | — | ||||||||||||
| Direct prerequisites – Strong prerequisite | none | ||||||||||||
| Direct prerequisites – Weak prerequisite | none | ||||||||||||
| Direct prerequisites – Parallel prerequisite | none | ||||||||||||
| Direct prerequisites – Milestone prerequisite | none | ||||||||||||
| Direct prerequisites – Exclusion | none |
Objectives
Programme
Lectures:
- Emerging problems in the field of economic analysis at multinational companies. Typical task types in media and text mining.
- Media and text analysis methods, search techniques, indexing, ranking procedures. Bag of words model. Information retrieval models: Boolean model and Vector model. Weighting schemes (tf-idf), cosine similarity. Query optimization. Web search, web mining.
- Text preprocessing steps. Tokenization, stemming algorithms, Porter, Lovins stemmers. Shallow and deep parsing. POS tagging. Syntax trees and dependency graphs. Stanford tools.
- Language detection, language dependence, Zipf's law. NLP (Natural Language Processing) tools.
- Named entity recognition, relation extraction from text. Typical approaches to relation extraction: co-occurrence, pattern matching methods, supervised machine learning methods. Opinion mining as a modern tool of market research.
- Use of deep neural networks in text analysis (LSTM - Long Short-Term Memory) and image and video content analysis (CNN - Convolutional Neural Network).
- Media classification for images and videos. Preprocessing steps. Types and methods of media classification. CBIR (Context-Based Image Retrieval), simple image processing procedures.
- Connecting image and text modalities. Deep learning methods and systems. Generative Adversarial Network (GAN).
- Reduction of the problem space of text corpora and media datasets, feature extraction and feature selection techniques.
- Text classification. Types and methods of text classification. Naïve Bayes classifier. Rocchio algorithm. Automatic text processing (text generation with deep learning).
- Chatbots and virtual assistants used in companies.
- Cost-effective classification. Active learning. Ensemble classifiers. Clustering media and text datasets.
- Single-label and multi-label text classification. Change tracking in classification tasks. Concept drift.
- Media recommendation systems.
The programming language used in the labs is Python with the appropriate program libraries.
Laboratories:
- Calculation of a weighting scheme (tf-idf) for a text corpus.
- Text preprocessing, indexing, stemming.
- Sentiment analysis.
- Digit recognition task with Keras program library.
- Application of deep learning methods.
- Effective classification (on text corpora, media datasets).
- Text mining application in the economic field.
The objective of the course is to introduce students to the content and information search services, from text processing to media streams. Students will learn about text and media search techniques, learn media and text analysis methods using deep learning techniques, and will be able to make decisions when designing corporate search systems and media content management systems.
Learning outcomes
Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:
Knowledge
No learning outcomes recorded.
Skills
No learning outcomes recorded.
Attitudes
No learning outcomes recorded.
Autonomy and responsibility
No learning outcomes recorded.
Oktatási módszertan
The laboratory exercises are grouped every two weeks, the other hours are lectures.
Tanulástámogató anyagok
Online források
Blanken, de Vries, Blok, Fres (eds): Multimedia Retrieval. Springer, 2007.; Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze: Introduction to Information Retrieval. Cambridge University Press, 2008; Ronen Feldman, James Sanger: The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data, Cambridge University Press, 2007
Recommended preliminary knowledge for completing the subject
Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
basic mathematical knowledge, probability theory
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
basic mathematical knowledge, probability theory
General rules
Requirements:
a. During the teaching period: a midterm test.
b. During the exam period: preparing the solution to the homework (started in the teaching period) (written), defending it in the exam (oral).
c. The condition for signature is to write at least to a sufficient level in the midterm test (including repeated midterm tests: see in the next point). The midterm test or repeated midterm test is successful if the student has reached the maximum score of at least 40%.
d. At least five of the laboratory exercises must be successfully completed to obtain a signature.
The exam consists of 2 parts: an oral examination of the entire course material of the semester and a defense of the previously submitted written homework. The 2 parts count 50-50% in the final mark.
Additional possibilities:
We provide an opportunity to make up for midterm test during the teaching period. For those who failed the midterm test and the repeated midterm test, we provide 1 opportunity for another (final) repeated midterm test. It is not possible to make up for the laboratory exercises. The condition for signature is to write one of the midterm test (first or repeated or final one) to at least a sufficient level and at least 5 successful laboratory exercises.
Assessment methods
In-term assessments
No detailed assessments provided.
Weight of in-term assessments
No weights provided.
Exam-period assessments
No detailed assessments provided.
Weight of exam elements
No weights provided.
Grade calculation
No grade thresholds provided.
Attendance requirements
No attendance requirements provided.
Rules for retake and resubmission
Not provided.
Short description
Not provided.
Detailed description
Not provided.
Recommended courses
Recommended: Data mining techniques
Workload to complete the subject
No workload breakdown provided.
Validity of subject requirements
Requirements valid from:
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Requirements valid until:
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Curriculum placement
No curriculum placements recorded for this subject version.