Gerhard Hagerer


  • Room: FMI 01.05.057
  • E-Mail (lectures/courses/PGdP/EIDI): hagererg(AT)
  • E-Mail (research/theses): ghagerer(AT)
  • Phone: +49-89-289-18684
  • Address: TUM - Fakultät für Informatik, Boltzmannstr. 3, 85747 Garching
  • Publications, bookmarks and bibliography


Current Research Interests

  • Deep Learning Methods for Natural Language Processing applied on Behavioral Economics Use Cases

No more topics for Winter Term 2019/2020!

I just leave the topics here as an overview.

Guided Research Topics for Winter Term 2019/2020

We have at least two offers for students who want to work on a guided research topic. The offer includes to summarize previous research, i.e., results from student theses and projects which have been conducted under my guidance as your guided research report. The idea is me guiding you to (learn to) write it as an actual paper which is to be published on an actual scientific conference. Besides of learning how to articulate and present yourself and your ideas when you want to write publication, you get the chance of a second authorship and according citations which both is a great contribution for your CV. Moreover, there might be the possibility that you have to run single experiments again and thus gaining machine learning skills and experience.

The actual topics are all about aspect-based sentiment analysis and varying aspect of machine and deep learning. Some currently relevant examples are the following:


Master Thesis Topics for Winter Term 2019/2020

Attention-Based Hierarchical Siamese Networks for Multi-Label Opinion Mining Problems on Sparse Data

Predicting Perception Uncertainty in Aspect-Based Sentiment Analysis (ABSA)

Cross-Lingual Aspect-Based Sentiment Analysis Using Semi-Supervised Learning and Deep Pre-Trained Embeddings

Implementing an Opinion Mining Framework for Crawling and Analyzing Social Media Using Unsupervised Semantic Similarity-Based Aspect Extraction

  • implement an integrated framework and web GUI for opinion researches to automatically perform the following steps:
    • automatically crawl given Facebook/Reddit groups and pages for texts of social media comments
    • define filters based on keywords for relevance
    • cluster the posts in an optimal way using semantic embeddings
      • XLING
      • ABAE
      • LSA
    • provide the option to label the extracted clusters using word lists
    • depict a graph of aspects and their corelation with each other
    • options: a) split up clusters, b) active learning loop to optimize relevance filter or number of clusters
    • produce according topic distributions regarding source, time, amount, and language
    • integrate a given sentiment analyis model 
  • The machine learning components are mostly already available and hyperparameters already known. The key idea is to apply, visualize, and -- in terms of human-computer interaction experiments -- evaluate deep learning based aspect extraction in terms of descriminative statistics, i.e., to give an semantically coherent overview of what humans on social media are talking about by using the most recent state-of-the-art techniques therefore.


Ongoing Theses

All of them are already taken. I just leave them here as an overview of my research.


Interesting Reads


Master Lab Course - Machine Learning and Natural Language Processing for Opinion Mining