14 research outputs found
FugueGenerator - Collaborative Melody Composition Based on a Generative Approach for Conveying Emotion in Music
(Abstract to follow
Combining content analysis and neural networks to analyze discussion topics in online comments about organic food
[EN] Consumers increasingly share their opinions about products in social media.
However, the analysis of this user-generated content is limited either to small,
in-depth qualitative analyses or to larger but often more superficial analyses
based on word frequencies. Using the example of online comments about
organic food, we investigate the relationship between qualitative analyses and
latest deep neural networks in three steps. First, a qualitative content analysis
defines a class system of opinions. Second, a pre-trained neural network, the
Universal Sentence Encoder, analyzes semantic features for each class. Third,
we show by manual inspection and descriptive statistics that these features
match with the given class structure from our qualitative study. We conclude
that semantic features from deep pre-trained neural networks have the
potential to serve for the analysis of larger data sets, in our case on organic
food. We exemplify a way to scale up sample size while maintaining the detail
of class systems provided by qualitative content analyses. As the USE is pretrained on many domains, it can be applied to different domains than organic
food and support consumer and public opinion researchers as well as
marketing practitioners in further uncovering the potential of insights from
user-generated content.Danner, H.; Hagerer, G.; Kasischke, F.; Groh, G. (2020). Combining content analysis and neural networks to analyze discussion topics in online comments about organic food. Editorial Universitat Politècnica de València. 211-219. https://doi.org/10.4995/CARMA2020.2020.11632OCS21121
An Analysis of Programming Course Evaluations Before and After the Introduction of an Autograder
Commonly, introductory programming courses in higher education institutions
have hundreds of participating students eager to learn to program. The manual
effort for reviewing the submitted source code and for providing feedback can
no longer be managed. Manually reviewing the submitted homework can be
subjective and unfair, particularly if many tutors are responsible for grading.
Different autograders can help in this situation; however, there is a lack of
knowledge about how autograders can impact students' overall perception of
programming classes and teaching. This is relevant for course organizers and
institutions to keep their programming courses attractive while coping with
increasing students.
This paper studies the answers to the standardized university evaluation
questionnaires of multiple large-scale foundational computer science courses
which recently introduced autograding. The differences before and after this
intervention are analyzed. By incorporating additional observations, we
hypothesize how the autograder might have contributed to the significant
changes in the data, such as, improved interactions between tutors and
students, improved overall course quality, improved learning success, increased
time spent, and reduced difficulty. This qualitative study aims to provide
hypotheses for future research to define and conduct quantitative surveys and
data analysis. The autograder technology can be validated as a teaching method
to improve student satisfaction with programming courses.Comment: Accepted full paper article on IEEE ITHET 202
Classification of Consumer Belief Statements From Social Media
Social media offer plenty of information to perform market research in order
to meet the requirements of customers. One way how this research is conducted
is that a domain expert gathers and categorizes user-generated content into a
complex and fine-grained class structure. In many of such cases, little data
meets complex annotations. It is not yet fully understood how this can be
leveraged successfully for classification. We examine the classification
accuracy of expert labels when used with a) many fine-grained classes and b)
few abstract classes. For scenario b) we compare abstract class labels given by
the domain expert as baseline and by automatic hierarchical clustering. We
compare this to another baseline where the entire class structure is given by a
completely unsupervised clustering approach. By doing so, this work can serve
as an example of how complex expert annotations are potentially beneficial and
can be utilized in the most optimal way for opinion mining in highly specific
domains. By exploring across a range of techniques and experiments, we find
that automated class abstraction approaches in particular the unsupervised
approach performs remarkably well against domain expert baseline on text
classification tasks. This has the potential to inspire opinion mining
applications in order to support market researchers in practice and to inspire
fine-grained automated content analysis on a large scale
A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion Mining
User-generated content from social media is produced in many languages,
making it technically challenging to compare the discussed themes from one
domain across different cultures and regions. It is relevant for domains in a
globalized world, such as market research, where people from two nations and
markets might have different requirements for a product. We propose a simple,
modern, and effective method for building a single topic model with sentiment
analysis capable of covering multiple languages simultanteously, based on a
pre-trained state-of-the-art deep neural network for natural language
understanding. To demonstrate its feasibility, we apply the model to newspaper
articles and user comments of a specific domain, i.e., organic food products
and related consumption behavior. The themes match across languages.
Additionally, we obtain an high proportion of stable and domain-relevant
topics, a meaningful relation between topics and their respective textual
contents, and an interpretable representation for social media documents.
Marketing can potentially benefit from our method, since it provides an
easy-to-use means of addressing specific customer interests from different
market regions around the globe. For reproducibility, we provide the code,
data, and results of our study.Comment: 10 pages, 2 tables, 5 figures, full paper, peer-reviewed, published
at KDIR/IC3k 2021 conferenc
End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment Analysis
Sentiment analysis is often a crowdsourcing task prone to subjective labels
given by many annotators. It is not yet fully understood how the annotation
bias of each annotator can be modeled correctly with state-of-the-art methods.
However, resolving annotator bias precisely and reliably is the key to
understand annotators' labeling behavior and to successfully resolve
corresponding individual misconceptions and wrongdoings regarding the
annotation task. Our contribution is an explanation and improvement for precise
neural end-to-end bias modeling and ground truth estimation, which reduces an
undesired mismatch in that regard of the existing state-of-the-art.
Classification experiments show that it has potential to improve accuracy in
cases where each sample is annotated only by one single annotator. We provide
the whole source code publicly and release an own domain-specific sentiment
dataset containing 10,000 sentences discussing organic food products. These are
crawled from social media and are singly labeled by 10 non-expert annotators.Comment: 10 pages, 2 figures, 2 tables, full conference paper, peer-reviewe
SocialVisTUM: An Interactive Visualization Toolkit for Correlated Neural Topic Models on Social Media Opinion Mining
Recent research in opinion mining proposed word embedding-based topic
modeling methods that provide superior coherence compared to traditional topic
modeling. In this paper, we demonstrate how these methods can be used to
display correlated topic models on social media texts using SocialVisTUM, our
proposed interactive visualization toolkit. It displays a graph with topics as
nodes and their correlations as edges. Further details are displayed
interactively to support the exploration of large text collections, e.g.,
representative words and sentences of topics, topic and sentiment
distributions, hierarchical topic clustering, and customizable, predefined
topic labels. The toolkit optimizes automatically on custom data for optimal
coherence. We show a working instance of the toolkit on data crawled from
English social media discussions about organic food consumption. The
visualization confirms findings of a qualitative consumer research study.
SocialVisTUM and its training procedures are accessible online.Comment: Demo paper accepted for publication on RANLP 2021; 8 pages, 5
figures, 1 tabl
GraphTMT: unsupervised graph-based topic modeling from video transcripts
To unfold the tremendous amount of multimedia data uploaded daily to social
media platforms, effective topic modeling techniques are needed. Existing work
tends to apply topic models on written text datasets. In this paper, we propose
a topic extractor on video transcripts. Exploiting neural word embeddings
through graph-based clustering, we aim to improve usability and semantic
coherence. Unlike most topic models, this approach works without knowing the
true number of topics, which is important when no such assumption can or should
be made. Experimental results on the real-life multimodal dataset MuSe-CaR
demonstrates that our approach GraphTMT extracts coherent and meaningful topics
and outperforms baseline methods. Furthermore, we successfully demonstrate the
applicability of our approach on the popular Citysearch corpus.Comment: JT and LS contributed equally to this wor