@article{8741949,
  abstract     = {{Since  the  rise  of  social  media,  the  authority  of  traditional  professional  literary  critics  has  beensupplemented – or undermined, depending on the point of view – by technological developmentsand the emergence of community-driven online layperson literary criticism.  So far, relatively littleresearch (Allington 2016, Kellermann et al. 2016, Kellermann and Mehling 2017, Bogaert 2017, Pi-anzola  et  al.  2020)  has  examined  this  layperson  user-generated  evaluative  “talk  of  literature”instead  of  addressing  traditional  forms  of  consecration.   In  this  paper,  we  examine  the  layper-son literary criticism pertaining to a prominent German-language literary award:  the Ingeborg-Bachmann-Preis, awarded during the Tage der deutschsprachigen Literatur (TDDL).We  propose  an  aspect-based  sentiment  analysis  (ABSA)  approach  to  discern  the  evaluativecriteria used to differentiate between ‘good’ and ‘bad’ literature.  To this end, we collected a cor-pus of German social media reviews, retrieved from Twitter, and enriched it with manual ABSAannotations:aspectsand aspect categories (e.g.  the motifs or themes in a text, the jury discus-sions and evaluations, ...),sentiment expressionsandnamed entities.  In a next step, the manualannotations are used as training data for our ABSA pipeline including 1) aspect term categoryprediction and 2) aspect term polarity classification.  Each pipeline component is developed usingstate-of-the-art pre-trained BERT models.Two sets of experiments were conducted for the aspect polarity detection:  one where only theaspect embeddings were used and another where an additional context window of five adjoiningwords in either direction of the aspect was considered.  We present the classification results forthe  aspect  category  and  aspect  sentiment  prediction  subtasks  for  the  Twitter  corpus.   Thesepreliminary experimental results show a good performance for the aspect category classification,with a macro and a weighted F1-score of 69% and 83% for the coarse-grained and 54% and 73% forthe fine-grained task, as well as for the aspect sentiment classification subtask, using an additionalcontext window, with a macro and a weighted F1-score of 70% and 71%, respectively}},
  author       = {{De Greve, Lore and Singh, Pranaydeep and Van Hee, Cynthia and Lefever, Els and Martens, Gunther}},
  issn         = {{2211-4009}},
  journal      = {{COMPUTATIONAL LINGUISTICS IN THE NETHERLANDS JOURNAL}},
  keywords     = {{Digital Humanities,BERT,Literary Prizes,Aspect-Based Sentiment Analysis,Sentiment Mining,Ingeborg-Bachmann-Preis,Social Media,Twitter,Literature,LT3}},
  language     = {{eng}},
  pages        = {{85--104}},
  title        = {{Aspect-based sentiment analysis for German : analyzing 'talk of literature' surrounding literary prizes on social media}},
  url          = {{https://www.clinjournal.org/index.php/clinj/issue/view/11}},
  volume       = {{11}},
  year         = {{2021}},
}