Emotions

Emotions have been extensively studied across disciplines, but are best defined within specific cultural contexts. In ancient Egypt, they are presented both as visceral experiences that may be “contained” within or transmitted from the heart or stomach, and as socially constructed strands of personhood. Emotions manifest in gestures, postures, and, to a lesser extent, facial expressions in Egyptian art; the presence or absence of their markers in humans may be connected to decorum and status. Animals are used both in art and script to represent emotional states. Various expressive terms exist to describe emotions linguistically, many of them compounds involving the heart, and emotional states are described in diverse genres of texts throughout time, particularly in New Kingdom love poetry. This discussion presents an overview of how emotions have been identified and studied in ancient Egypt and suggests possible future avenues and domains for research

Emotion-corpus guided lexicons for sentiment analysis on Twitter.

Research in Psychology have proposed frameworks that map emotion concepts with sentiment concepts. In this paper we study this mapping from a computational modelling perspective with a view to establish the role of an emotion-rich corpus for lexicon-based sentiment analysis. We propose two different methods which harness an emotion-labelled corpus of tweets to learn world-level numerical quantification of sentiment strengths over a positive to negative spectrum. The proposed methods model the emotion corpus using a generative unigram mixture model (UMM), combined with the emotion-sentiment mapping proposed in Psychology [6] for automated generation of sentiment lexicons. Sentiment analsysis experiments on benchmark Twitter data sets confirm the equality of our proposed lexicons. Further a comparative analysis with standard sentiment lexicons suggest that the proposed lexicons lead to a significantly better performance in both sentimentclassification and sentiment intensity prediction tasks

Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis

In this paper, we propose a two-layered multi-task attention based neural network that performs sentiment analysis through emotion analysis. The proposed approach is based on Bidirectional Long Short-Term Memory and uses Distributional Thesaurus as a source of external knowledge to improve the sentiment and emotion prediction. The proposed system has two levels of attention to hierarchically build a meaningful representation. We evaluate our system on the benchmark dataset of SemEval 2016 Task 6 and also compare it with the state-of-the-art systems on Stance Sentiment Emotion Corpus. Experimental results show that the proposed system improves the performance of sentiment analysis by 3.2 F-score points on SemEval 2016 Task 6 dataset. Our network also boosts the performance of emotion analysis by 5 F-score points on Stance Sentiment Emotion Corpus.Comment: Accepted in the Proceedings of The 2019 IEEE International Joint Conference on Neural Networks (IJCNN 2019

Evaluating Emotional Nuances in Dialogue Summarization

Automatic dialogue summarization is a well-established task that aims to identify the most important content from human conversations to create a short textual summary. Despite recent progress in the field, we show that most of the research has focused on summarizing the factual information, leaving aside the affective content, which can yet convey useful information to analyse, monitor, or support human interactions. In this paper, we propose and evaluate a set of measures $PEmo$, to quantify how much emotion is preserved in dialog summaries. Results show that, summarization models of the state-of-the-art do not preserve well the emotional content in the summaries. We also show that by reducing the training set to only emotional dialogues, the emotional content is better preserved in the generated summaries, while conserving the most salient factual information

Sentiment and Sentence Similarity as Predictors of Integrated and Independent L2 Writing Performance

This study aimed to utilize sentiment and sentence similarity analyses, two Natural Language Processing techniques, to see if and how well they could predict L2 Writing Performance in integrated and independent task conditions. The data sources were an integrated L2 writing corpus of 185 literary analysis essays and an independent L2 writing corpus of 500 argumentative essays, both of which were compiled in higher education contexts. Both essay groups were scored between 0 and 100. Two Python libraries, TextBlob and SpaCy, were used to generate sentiment and sentence similarity data. Using sentiment (polarity and subjectivity) and sentence similarity variables, regression models were built and 95% prediction intervals were compared for integrated and independent corpora. The results showed that integrated L2 writing performance could be predicted by subjectivity and sentence similarity. However, only subjectivity predicted independent L2 writing performance. The prediction interval of subjectivity for independent writing model was found to be narrower than the same interval for integrated writing. The results show that the sentiment and sentence similarity analysis algorithms can be used to generate complementary data to improve more complex multivariate L2 writing performance prediction models

Blended learning in the wake of ICT infrastructure deficiencies: The case of a Zimbabwean University

In the wake of debates between actors in the Zimbabwean higher education sector about the effectiveness of e-learning models, it is important to investigate the effectiveness of using blended learning at a time when infrastructure challenges are disrupting ICT access. This paper aims to address this quest for a deeper understanding by investigating students\u27 perceptions of blended learning at a selected Zimbabwean university. Twelve in-depth interviews were conducted with students from a Zimbabwean university that employs blended learning under an interpretivist paradigm. Vygotsky\u27s Zone of Proximal Development (ZPD) was used for conceptualising students\u27 cognitive development and Engestrom\u27s (2003) Third-generation Activity Theory(AT) as a framework for assessing the home and the university activity systems that characterise blended learning. Findings show that blended learning can be implemented in universities with poor ICT infrastructure since asynchronous blended learning using learning management systems such as Moodle allows content to be downloaded from connected areas for offline study. The study contributes to policies on the implementation of blended learning in institutions of higher learning by showing how it enables cognitive development in the ZPD