Florian Strohm

Institute for Visualisation and Interactive Systems, University of Stuttgart

florian.strohm@vis.uni-stuttgart.de +49 711 685 61723 Pfaffenwaldring 5a, 70569 Stuttgart, Germany University of Stuttgart, SimTech Building, Room 01.031

Biography

Florian Strohm is a PhD student in the Perceptual User Interfaces group since January 2020. He holds a Bachelor's and Master’s degree in Computer Science from the University of Stuttgart. During his Master’s studies he focused on the fields of Computer Vision and Machine Learning. His research interests are analyzing cognitive processes such as how humans encode visual stimuli in memory and the synthetic reconstruction of such stimuli.

Teaching

2020

Machine Learning and Computer Vision for HCI (Fachpraktikum)Master

2020

Mensch-Computer-Interaktion (Tutor)Bachelor

2018

Information Retrieval and Text Mining (Teaching Assistant)Bachelor/Master

2017

Rechnerorganisation (Tutor)Bachelor

Publications

1. 

   An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs

   Florian Strohm, Roman Klinger

   IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 673-681, 2018.

   Abstract Links BibTeX Project

   The effect of amplifiers, downtoners, and negations has been studied in general and particularly in the context of sentiment analysis. However, there is only limited work which aims at transferring the results and methods to discrete classes of emotions, e.g., joy, anger, fear, sadness, surprise, and disgust. For instance, it is not straight-forward to interpret which emotion the phrase "not happy" expresses. With this paper, we aim at obtaining a better understanding of such modifiers in the context of emotion-bearing words and their impact on document-level emotion classification, namely, microposts on Twitter. We select an appropriate scope detection method for modifiers of emotion words, incorporate it in a document-level emotion classification model as additional bag of words and show that this approach improves the performance of emotion classification. In addition, we build a term weighting approach based on the different modifiers into a lexical model for the analysis of the semantics of modifiers and their impact on emotion meaning. We show that amplifiers separate emotions expressed with an emotion-bearing word more clearly from other secondary connotations. Downtoners have the opposite effect. In addition, we discuss the meaning of negations of emotion-bearing words. For instance we show empirically that "not happy" is closer to sadness than to anger and that fear words in the scope of downtoners often express surprise.

   doi:10.1109/DSAA.2018.00087

   strohm18_dsaa.pdf

   @inproceedings{strohm18_dsaa, author = {Strohm, Florian and Klinger, Roman}, booktitle = {IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)}, title = {An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs}, year = {2018}, pages = {673-681}, doi = {10.1109/DSAA.2018.00087} }