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Found 80 papers, 14 papers with code
A Computational Analysis of Financial and Environmental Narratives within Financial Reports and its Value for Investors
We answer this question by training and evaluating an end-to-end deep learning approach (based on BERT and GloVe embeddings) to predict the financial and environmental performance of the company from the “Management’s Discussion and Analysis of Financial Conditions and Results of Operations” (MD&A) section of 10-K (yearly) and 10-Q (quarterly) filings.
“splink” is happy and “phrouth” is scary: Emotion Intensity Analysis for Nonsense Words
This raises the question if the association is purely a product of the learned affective imports inherent to semantic meanings, or is also an effect of other features of words, e. g., morphological and phonological patterns.
"You are an expert annotator": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling
Labeling corpora constitutes a bottleneck to create models for new tasks or domains.
Can Factual Statements be Deceptive? The DeFaBel Corpus of Belief-based Deception
As the argumentation stems from genuine belief, it may be unlikely to exhibit the linguistic properties associated with deception or lying.
Understanding Fine-grained Distortions in Reports of Scientific Findings
Distorted science communication harms individuals and society as it can lead to unhealthy behavior change and decrease trust in scientific institutions.
English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts
This is particularly of interest when we have access to a multilingual large language model, because we could request labels with English prompts even for non-English data.
What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verification
In a study with trained annotation experts we prompt them to find evidence for biomedical claims, and observe how they refine search queries for their evidence search.
Topic Bias in Emotion Classification
when funeral events are over-represented for instances labeled with sadness, despite the emotion of pride being more appropriate here.
Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches
(1) Emotions are events; and this perspective is the fundament in natural language processing for emotion role labeling.
Emotion-Conditioned Text Generation through Automatic Prompt Optimization
We evaluate the method on emotion-conditioned text generation with a focus on event reports and compare it to manually designed prompts that also act as the seed for the optimization procedure.
Affective Natural Language Generation of Event Descriptions through Fine-grained Appraisal Conditions
They put the assessment of the situation on the spot, for instance regarding the own control or the responsibility for what happens.
UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception Detection
Verbal deception has been studied in psychology, forensics, and computational linguistics for a variety of reasons, like understanding behaviour patterns, identifying false testimonies, and detecting deception in online communication.
Automatic Emotion Experiencer Recognition
We show that experiencer detection in text is a challenging task, with a precision of . 82 and a recall of . 56 (F1 =. 66).
An Entity-based Claim Extraction Pipeline for Real-world Biomedical Fact-checking
This mismatch can be mitigated by adapting the social media input to mimic the focused nature of common training claims.
Experiencer-Specific Emotion and Appraisal Prediction
Emotion classification in NLP assigns emotions to texts, such as sentences or paragraphs.
Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets
To make user-generated content checkable by existing models, we propose to reformulate the social-media input in such a way that the resulting claim mimics the claim characteristics in established datasets.
Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora
This raises the question how to prompt a natural language inference model for zero-shot learning emotion classification.
Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction
We analyze the suitability of appraisal theories for emotion analysis in text with the goal of understanding if appraisal concepts can reliably be reconstructed by annotators, if they can be predicted by text classifiers, and if appraisal concepts help to identify emotion categories.
CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets
The corpus consists of 300 tweets, each annotated with medical named entities and relations.
Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)
Named entity recognition and relation extraction are methods to structure information that is available in unstructured text.
x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations
In addition, we link them to the event they found salient (which can be different for different experiencers in a text) by annotating event properties, or appraisals (e. g., the perceived event undesirability, the uncertainty of its outcome).
"splink" is happy and "phrouth" is scary: Emotion Intensity Analysis for Nonsense Words
This raises the question if the association is purely a product of the learned affective imports inherent to semantic meanings, or is also an effect of other features of words, e. g., morphological and phonological patterns.
Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference
We do this by testing how well the pre-trained models perform on the \alpha{}nli task when just comparing sentence embeddings with cosine similarity to what the performance that is achieved when training a classifier on top of these embeddings.
Items from Psychometric Tests as Training Data for Personality Profiling Models of Twitter Users
We investigate this approach for personality profiling, and evaluate BERT classifiers fine-tuned on such psychometric test items for the big five personality traits (openness, conscientiousness, extraversion, agreeableness, neuroticism) and analyze various augmentation strategies regarding their potential to address the challenges coming with such a small corpus.
On the Complementarity of Images and Text for the Expression of Emotions in Social Media
The emotions of anger and sadness are best predicted with a multimodal model, while text alone is sufficient for disgust, joy, and surprise.
From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey
With this paper, we aim at providing a comprehensive discussion of the styles that have received attention in the transfer task.
Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language
The recognition of hate speech and offensive language (HOF) is commonly formulated as a classification task to decide if a text contains HOF.
Emotion Recognition under Consideration of the Emotion Component Process Model
It states that emotions are a coordinated process of various subcomponents, in reaction to an event, namely the subjective feeling, the cognitive appraisal, the expression, a physiological bodily reaction, and a motivational action tendency.
Emotion Stimulus Detection in German News Headlines
Emotion stimulus extraction is a fine-grained subtask of emotion analysis that focuses on identifying the description of the cause behind an emotion expression from a text passage (e. g., in the sentence "I am happy that I passed my exam" the phrase "passed my exam" corresponds to the stimulus.).
Constraining Linear-chain CRFs to Regular Languages
However, the CRF's Markov assumption makes it impossible for CRFs to represent distributions with \textit{nonlocal} dependencies, and standard CRFs are unable to respect nonlocal constraints of the data (such as global arity constraints on output labels).
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Semantic Role Labeling
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Claim Detection in Biomedical Twitter Posts
We aim to fill this research gap and annotate a corpus of 1200 tweets for implicit and explicit biomedical claims (the latter also with span annotations for the claim phrase).
Hate Towards the Political Opponent: A Twitter Corpus Study of the 2020 US Elections on the Basis of Offensive Speech and Stance Detection
Further, we annotate if the tweet is written in an offensive style.
Emotion Ratings: How Intensity, Annotation Confidence and Agreements are Entangled
When humans judge the affective content of texts, they also implicitly assess the correctness of such judgment, that is, their confidence.
Emotion-Aware, Emotion-Agnostic, or Automatic: Corpus Creation Strategies to Obtain Cognitive Event Appraisal Annotations
Appraisal theories explain how the cognitive evaluation of an event leads to a particular emotion.
Lost in Back-Translation: Emotion Preservation in Neural Machine Translation
Machine translation provides powerful methods to convert text between languages, and is therefore a technology enabling a multilingual world.
Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions?
Is it a particular target (everybody loves X) or a stimulus (doing X makes everybody sad)?
Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus Detection
"), and (3) as clause classification ("Does this clause contain the emotion stimulus?").
Dissecting Span Identification Tasks with Performance Prediction
Span identification (in short, span ID) tasks such as chunking, NER, or code-switching detection, ask models to identify and classify relevant spans in a text.
Challenges in Emotion Style Transfer: An Exploration with a Lexical Substitution Pipeline
We propose the task of emotion style transfer, which is particularly challenging, as emotions (here: anger, disgust, fear, joy, sadness, surprise) are on the fence between content and style.
Appraisal Theories for Emotion Classification in Text
Automatic emotion categorization has been predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory, for instance following the fundamental emotion classes proposed by Paul Ekman (fear, joy, anger, disgust, sadness, surprise) or Robert Plutchik (adding trust, anticipation).
PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry
Thus, we conceptualize a set of aesthetic emotions that are predictive of aesthetic appreciation in the reader, and allow the annotation of multiple labels per line to capture mixed emotions within their context.
Automatic Section Recognition in Obituaries
Obituaries contain information about people's values across times and cultures, which makes them a useful resource for exploring cultural history.
GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception
Most research on emotion analysis from text focuses on the task of emotion classification or emotion intensity regression.
Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning
We use off-the-shelf tools for emotion detection in audio and face and compare that to a neural transfer learning approach for emotion recognition from text which utilizes existing resources from other domains.
An Analysis of Emotion Communication Channels in Fan-Fiction: Towards Emotional Storytelling
Our analysis shows that stories written by humans convey character emotions along various non-verbal channels.
Embedding Projection for Targeted Cross-Lingual Sentiment: Model Comparisons and a Real-World Study
As expected, the choice of annotated source language for projection to a target leads to better results for source-target language pairs which are similar.
An Analysis of Emotion Communication Channels in Fan Fiction: Towards Emotional Storytelling
Our analysis shows that stories written by humans convey character emotions along various non-verbal channels.
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English
Sentiment analysis has a range of corpora available across multiple languages.
Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis
Adjective phrases like "a little bit surprised", "completely shocked", or "not stunned at all" are not handled properly by currently published state-of-the-art emotion classification and intensity prediction systems which use pre-dominantly non-contextualized word embeddings as input.
Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters
The development of a fictional plot is centered around characters who closely interact with each other forming dynamic social networks.
Adversarial Training for Satire Detection: Controlling for Confounding Variables
We therefore propose a novel model for satire detection with an adversarial component to control for the confounding variable of publication source.
DERE: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction
As a result, comparability of models across tasks is missing and their applicability to new tasks is limited.
IEST: WASSA-2018 Implicit Emotions Shared Task
Here, for the first time, we propose a shared task where systems have to predict the emotions in a large automatically labeled dataset of tweets without access to words denoting emotions.
An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs
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.
A Survey on Sentiment and Emotion Analysis for Computational Literary Studies
Emotions are a crucial part of compelling narratives: literature tells us about people with goals, desires, passions, and intentions.
An Analysis of Annotated Corpora for Emotion Classification in Text
Based on this aggregation, we perform the first cross-corpus classification experiments in the spirit of future research enabled by this paper, in order to gain insight and a better understanding of differences of models inferred from the data.
Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse Domains
Our analysis shows that our model performs comparably to state-of-the-art approaches on domains that are similar, while performing significantly better on highly divergent domains.
Who Feels What and Why? Annotation of a Literature Corpus with Semantic Roles of Emotions
We aim at filling this gap and present a publicly available corpus based on Project Gutenberg, REMAN (Relational EMotion ANnotation), manually annotated for spans which correspond to emotion trigger phrases and entities/events in the roles of experiencers, targets, and causes of the emotion.
SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling
Supervised machine learning algorithms require training data whose generation for complex relation extraction tasks tends to be difficult.
Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment Analysis in Diverse Domains
Our analysis shows that our model performs comparably to state-of-the-art approaches on domains that are similar, while performing significantly better on highly divergent domains.
Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages
Sentiment analysis in low-resource languages suffers from a lack of annotated corpora to estimate high-performing models.
Cross-Lingual Sentiment Classification
Machine Translation
+5
Predicting Disease-Gene Associations using Cross-Document Graph-based Features
In the context of personalized medicine, text mining methods pose an interesting option for identifying disease-gene associations, as they can be used to generate novel links between diseases and genes which may complement knowledge from structured databases.
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets
We show that Bi-LSTMs perform well across datasets and that both LSTMs and Bi-LSTMs are particularly good at fine-grained sentiment tasks (i. e., with more than two classes).
Towards Confidence Estimation for Typed Protein-Protein Relation Extraction
In this paper, we discuss this task and propose different approaches for confidence estimation and a pipeline to evaluate such methods.
IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning
Our submission to the WASSA-2017 shared task on the prediction of emotion intensity in tweets is a supervised learning method with extended lexicons of affective norms.
Ranking Right-Wing Extremist Social Media Profiles by Similarity to Democratic and Extremist Groups
Social media are used by an increasing number of political actors.
Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus
There is a rich variety of data sets for sentiment analysis (viz., polarity and subjectivity classification).
Investigating the Relationship between Literary Genres and Emotional Plot Development
Literary genres are commonly viewed as being defined in terms of content and stylistic features.
Opinion Mining in Online Reviews About Distance Education Programs
The popularity of distance education programs is increasing at a fast pace.
SCARE ― The Sentiment Corpus of App Reviews with Fine-grained Annotations in German
This corpus consists of 1, 760 annotated application reviews from the Google Play Store with 2, 487 aspects and 3, 959 subjective phrases.
The USAGE review corpus for fine grained multi lingual opinion analysis
Contributing to this situation, this paper describes the Bielefeld University Sentiment Analysis Corpus for German and English (USAGE), which we offer freely to the community and which contains the annotation of product reviews from Amazon with both aspects and subjective phrases.
