Master Lab Course - Machine Learning and Natural Language Processing for Opinion Mining (IN2106, IN4249)
Course 0000004166 in SS 2019
General Data
| Course Type | practical training |
|---|---|
| Semester Weekly Hours | 6 SWS |
| Organisational Unit | Informatics 2 - Chair of Formal Languages, Compiler Construction, Software Construction (Prof. Seidl) |
| Lecturers |
Gerhard Johann Hagerer Responsible/Coordination: Georg Groh |
| Dates |
Wed, 08:00–11:00, MI 01.10.011 and 1 singular or moved dates |
Assignment to Modules
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IN2106: Master-Praktikum / Advanced Practical Course
This module is included in the following catalogs:- Further Modules from Other Disciplines
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | Nowadays, Social Media are widely used to exchange and communicate feelings and impressions about things we encounter in our daily life. As a consequence, a plethora of text data is available online containing different kinds of sentiments about a range of topics. We aim at structuring this ocean of information in terms of a representation which is comprehensible for opinion researchers from a non-technical domain. Therefore recent advances in Machine Learning and Natural Language Processing appear promising, since these mimic human understanding of text by including and extracting universal semantic knowledge from large textual resources. Therefore, we provide a range of related research literature based on which students implement and apply novel methods for sentiment analysis, semantic clustering, aspect extraction, topic modeling, and data pre-processing and collection. The methods potentially incorporate neural networks and among others involve semantic embeddings, attention mechanisms, transfer learning, clustering algorithms, and unsupervised as well as supervised approaches. |
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| Links |
E-Learning course (e. g. Moodle) TUMonline entry |