Exploring Multimodal Sentiment Analysis in Plays: A Case Study for a Theater Recording of Emilia Galotti

We present first results of an exploratory study about sentiment analysis via different media channels on a German historical play. We propose the exploration of other media channels than text for sentiment analysis on plays since the auditory and visual channel might offer important cues for sentiment analysis. We perform a case study and investigate how textual, auditory (voice-based), and visual (face-based) sentiment analysis perform compared to human annotations and how these approaches differ from each other. As use case we chose Emilia Galotti by the famous German playwright Gotthold Ephraim Lessing. We acquired a video recording of a 2002 theater performance of the play at the “Wiener Burgtheater”. We evaluate textual lexicon-based sentiment analysis and two state-of-the-art audio and video sentiment analysis tools. As gold standard we use speech-based annotations of three expert annotators. We found that the audio and video sentiment analysis do not perform better than the textual sentiment analysis and that the presentation of the video channel did not improve annotation statistics. We discuss the reasons for this negative result and limitations of the approaches. We also outline how we plan to further investigate the possibilities of multimodal sentiment analysis

Distant Reading Sentiments and Emotions in Historic German Plays

Sentiment and emotions are important parts of the analysis and interpretation of literary texts, especially of plays. Therefore, the computational method to analyze sentiments and emotions in written text, sentiment analysis, has found its way into computational literary studies. However, recent research in computational literary studies is focused on annotation and the evaluation of different approaches. We present a tool to investigate the possibilities of Distant Reading the sentiments and emotions expressed in the plays of Lessing. Researchers can explore polarity and emotion distributions and progression on concerning structural and character based levels but also character relations. We present various use cases to highlight the visualizations and functionalities of our tool and discuss how Distant Reading of sentiments can add value to research in literary studies

Emotional Storyteller for Vision Impaired and Hearing-Impaired Children

Tellie is an innovative mobile app designed to offer an immersive and emotionally enriched storytelling experience for children who are visually and hearing impaired. It achieves this through four main objectives: Text extraction utilizes the CRAFT model and a combination of Convolutional Neural Networks (CNNs), Connectionist Temporal Classification (CTC), and Long Short-Term Memory (LSTM) networks to accurately extract and recognize text from images in storybooks. Recognition of Emotions in Sentences employs BERT to detect and distinguish emotions at the sentence level including happiness, anger, sadness, and surprise. Conversion of Text to Human Natural Audio with Emotion transforms text into emotionally expressive audio using Tacotron2 and Wave Glow, enhancing the synthesized speech with emotional styles to create engaging audio narratives. Conversion of Text to Sign Language: To cater to the Deaf and hard-of-hearing community, Tellie translates text into sign language using CNNs, ensuring alignment with real sign language expressions. These objectives combine to create Tellie, a groundbreaking app that empowers visually and hearing-impaired children with access to captivating storytelling experiences, promoting accessibility and inclusivity through the harmonious integration of language, creativity, and technology. This research demonstrates the potential of advanced technologies in fostering inclusive and emotionally engaging storytelling for all children

Katharsis – A Tool for Computational Drametrics

We present Katharsis, a tool for "computational drametrics" that implements Solomon Marcus' (1973) theory of mathematical drama analysis. The tool computes and visualizes character configurations and speech statistics for different levels of analysis and allows users to compare different collections of plays. We illustrate the usefulness of the tool for literary studies via several use cases. The tool is freely available online for a test corpus of approximately 100 German plays: http://lauchblatt.github.io/Katharsis/index.htm

„Kann man denn auch nicht lachend sehr ernsthaft sein?" – Zum Einsatz von Sentiment Analyse-Verfahren für die quantitative Untersuchung von Lessings Dramen

Der Beitrag beschäftigt sich mit dem Einsatz von Sentiment Analysis im Bereich der Dramenanalyse. Es werden erstmals systematisch verschiedene Methoden der Sentiment Analysis für Dramen getestet und evaluiert. Zudem wird exploriert, inwiefern bisher in der Literaturwissenschaft erforschte Aspekte von Dramen mithilfe der Sentiment Analysis erfasst werden und inwiefern die Sentiment Analysis auch für die Gewinnung neuer literaturwissenschaftlicher Erkenntnisse eingesetzt werden kann. Das im Rahmen dieser Studie verwendete Lessing-Korpus umfasst ein mit Strukturinformationen annotiertes Dramenkorpus mit 11 Dramen, bestehend aus insgesamt 8224 Einzelrepliken. Sämtliche Dramen wurden über die Plattform TextGrid bezogen, so dass alle im Rahmen dieses Beitrags entwickelten Tools auch auf andere TextGrid-Dramen anwendbar sind. Mit dem am besten evaluierten Sentiment Analysis-Verfahren wurde eine webbasierte Anwendung zur Analyse und Visualisierung von Sentiment-Verteilungen und -Verläufen implementiert

An Expressive Poetry Reader

We present SPARSAR, a system for the automatic analysis of poetry(and text) style. It makes use of NLP tools like tokenizers, sentence splitters, NER (Name Entity Recognition) tools, and a tagger. In addition, it adds syntactic and semantic structural analysis and prosodic modeling. We use a constituency parser to measure the structure of modifiers in NPs; and a dependency mapping to analyse the verbal complex and determine Polarity and Factuality. A phonological parser is used to account for OOVWs, in the process of grapheme to phoneme conversion of the poem. We also measure the prosody of the poem by associating mean durational values in msecs to each syllable from a database of syllable durations; to account for missing syllables we use a syllable parser. Eventually we produce six general indices that allow single poems as well as single poets to be compared. A fundamental component for the production of emotions is the one that performs affective and sentiment analysis. Lines associated to specific emotions are then marked to be pronounced with special care for the final module of the system, which is responsible for the production of expressive reading by a TTS system. Expressive reading is allowed by the possibility to interact with the TTS by means of specific markers and parameters

Live Sentiment Annotation of Movies via Arduino and a Slider

In this contribution, we present the first version of a novel approach and prototype to perform live sentiment annotation of movies while watching them. Our prototype consists of an Arduino microcontroller and a potentiometer, which is paired with a slider. We motivate the need for this approach by arguing that the presentation of multimedia content of movies as well as performing the annotation live during the viewing of the movie is beneficial for the annotation process and more intuitive for the viewer/annotator. After outlining the motivation and the technical setup of our system, we report on which studies we plan to validate the benefits of our system

Sentiment Annotation of Historic German Plays: An Empirical Study on Annotation Behavior

We present results of a sentiment annotation study in the context of historical German plays. Our annotation corpus consists of 200 representative speeches from the German playwright Gotthold Ephraim Lessing. Six annotators, five non-experts and one expert in the domain, annotated the speeches according to different sentiment annotation schemes. They had to annotate the differentiated polarity (very negative, negative, neutral, mixed, positive, very positive), the binary polarity (positive/negative) and the occurrence of eight basic emotions. After the annotation, the participants completed a questionnaire about their experience of the annotation process; additional feedback was gathered in a closing interview. Analysis of the annotations shows that the agreement among annotators ranges from low to mediocre. The non-expert annotators perceive the task as very challenging and report different problems in understanding the language and the context. Although fewer problems occur for the expert annotator, we cannot find any differences in the agreement levels among non-experts and between the expert and the non-experts. At the end of the paper, we discuss the implications of this study and future research plans for this area

SentText: A Tool for Lexicon-based Sentiment Analysis in Digital Humanities

We present SentText, a web-based tool to perform and explore lexicon-based sentiment analysis on texts, specifically developed for the Digital Humanities (DH) community. The tool was developed integrating ideas of the user-entered design process and we gathered requirements via semi-structured interviews. The tool offers the functionality to perform sentiment analysis with predefined sentiment lexicons or self-adjusted lexicons. Users can explore results of sentiment analysis via various visualizations like bar or pie charts and word clouds. It is also possible to analyze and compare collections of documents. Furthermore, we have added a close reading function enabling researchers to examine the applicability of sentiment lexicons for specific text sorts. We report upon the first usability tests with positive results. We argue that the tool is beneficial to explore lexicon-based sentiment analysis in the DH but can also be integrated in DH-teaching

An Evaluation of Lexicon-based Sentiment Analysis Techniques for the Plays of Gotthold Ephraim Lessing

We present results from a project on sentiment analysis of drama texts, more concretely the plays of Gotthold Ephraim Lessing. We conducted an annotation study to create a gold standard for a systematic evaluation. The gold standard consists of 200 speeches of Lessing’s plays and was manually annotated with sentiment information by five annotators. We use the gold standard data to evaluate the performance of different German sentiment lexicons and processing configurations like lemmatization, the extension of lexicons with historical linguistic variants, and stop words elimination, to explore the influence of these parameters and to find best practices for our domain of application. The best performing configuration accomplishes an accuracy of 70%. We discuss the problems and challenges for sentiment analysis in this area and describe our next steps toward further research