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Found 57 papers, 21 papers with code
TUSC: Emotion Word Usage in Tweets from US and Canada
Over the last decade, Twitter has emerged as one of the most influential forums for social, political, and health discourse.
Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers
Research in psychopathology has shown that, at an aggregate level, the patterns of emotional change over time -- emotion dynamics -- are indicators of one's mental health.
We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields
We analyzed ~77k NLP papers, ~3. 1m citations from NLP papers to other papers, and ~1. 8m citations from other papers to NLP papers.
Utterance Emotion Dynamics in Children's Poems: Emotional Changes Across Age
These results act as a useful baselines for further research in how patterns of emotions expressed by children change with age, and their association with mental health.
Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis
By running experiments on 18 diverse datasets in 9 languages, we show that despite being markedly poor at instance level emotion classification, LexO methods are highly accurate at generating emotion arcs when aggregating information from hundreds of instances.
Forgotten Knowledge: Examining the Citational Amnesia in NLP
Citing papers is the primary method through which modern scientific writing discusses and builds on past work.
A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?
In this study, we propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques.
The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research
Recent advances in deep learning methods for natural language processing (NLP) have created new business opportunities and made NLP research critical for industry development.
SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)
We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github. com/afrisenti-semeval/afrisent-semeval-2023.
AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
These include 75 languages with at least one million speakers each.
Geographic Citation Gaps in NLP Research
Similar disparities are also believed to exist for paper citation counts.
Frustratingly Easy Sentiment Analysis of Text Streams: Generating High-Quality Emotion Arcs Using Emotion Lexicons
We also compare two common ways of generating emotion arcs: Machine-Learning (ML) models and Lexicon-Only (LexO) methods.
CS-Insights: A System for Analyzing Computer Science Research
This paper presents CS-Insights, an interactive web application to analyze computer science publications from DBLP through multiple perspectives.
Best Practices in the Creation and Use of Emotion Lexicons
Words play a central role in how we express ourselves.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research
We present an initial analysis focused on the volume of computer science research (e. g., number of papers, authors, research activity), trends in topics of interest, and citation patterns.
Tweet Emotion Dynamics: Emotion Word Usage in Tweets from US and Canada
Over the last decade, Twitter has emerged as one of the most influential forums for social, political, and health discourse.
What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study
We show that human intuition regarding relatedness of sentence pairs is highly reliable, with a repeat annotation correlation of 0. 84.
Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis
The objective of the sheet is to facilitate and encourage more thoughtfulness on why to automate, how to automate, and how to judge success well before the building of AER systems.
Ethics Sheets for AI Tasks
I will also present a template for ethics sheets with 50 ethical considerations, using the task of emotion recognition as a running example.
Ruddit: Norms of Offensiveness for English Reddit Comments
On social media platforms, hateful and offensive language negatively impact the mental well-being of users and the participation of people from diverse backgrounds.
Emotion Dynamics in Movie Dialogues
In this paper, we introduce a framework to track emotion dynamics through one's utterances.
Practical and Ethical Considerations in the Effective use of Emotion and Sentiment Lexicons
Lexicons of word-emotion associations are widely used in research and real-world applications.
SOLO: A Corpus of Tweets for Examining the State of Being Alone
We use SOLO to analyze the language and emotions associated with the state of being alone.
NLP Scholar: An Interactive Visual Explorer for Natural Language Processing Literature
As part of the NLP Scholar project, we created a single unified dataset of NLP papers and their meta-information (including citation numbers), by extracting and aligning information from the ACL Anthology and Google Scholar.
Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Text
Recent advances in machine learning have led to computer systems that are human-like in behaviour.
Gender Gap in Natural Language Processing Research: Disparities in Authorship and Citations
Disparities in authorship and citations across gender can have substantial adverse consequences not just on the disadvantaged genders, but also on the field of study as a whole.
Examining Citations of Natural Language Processing Literature
We extracted information from the ACL Anthology (AA) and Google Scholar (GS) to examine trends in citations of NLP papers.
WordWars: A Dataset to Examine the Natural Selection of Words
We explore three broad questions with the dataset: (1) what is the degree to which predominant words in these synsets have changed, (2) how do prominent word features such as frequency, length, and concreteness impact natural selection, and (3) what are the differences between the predominant words of the 2000s and the predominant words of early 1800s.
NLP Scholar: A Dataset for Examining the State of NLP Research
We aligned the information in the AA and Google Scholar datasets to create the NLP Scholar Dataset {--} a single unified source of information (from both AA and Google Scholar) for tens of thousands of NLP papers.
PoKi: A Large Dataset of Poems by Children
We use non-parametric regressions to model developmental differences from early childhood to late-adolescence.
SemEval-2015 Task 10: Sentiment Analysis in Twitter
In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter.
The Natural Selection of Words: Finding the Features of Fitness
We introduce a dataset for studying the evolution of words, constructed from WordNet and the Google Books Ngram Corpus.
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.
Sentiment Composition of Words with Opposing Polarities
In this paper, we explore sentiment composition in phrases that have at least one positive and at least one negative word---phrases like 'happy accident' and 'best winter break'.
NRC-Canada at SMM4H Shared Task: Classifying Tweets Mentioning Adverse Drug Reactions and Medication Intake
Our team, NRC-Canada, participated in two shared tasks at the AMIA-2017 Workshop on Social Media Mining for Health Applications (SMM4H): Task 1 - classification of tweets mentioning adverse drug reactions, and Task 2 - classification of tweets describing personal medication intake.
Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems
Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases.
Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation
Rating scales are a widely used method for data annotation; however, they present several challenges, such as difficulty in maintaining inter- and intra-annotator consistency.
Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best-Worst Scaling
Access to word-sentiment associations is useful for many applications, including sentiment analysis, stance detection, and linguistic analysis.
The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition
Using phrasal terms in the created dataset, we analyze the impact of individual modifiers and the average effect of the groups of modifiers on overall sentiment.
WASSA-2017 Shared Task on Emotion Intensity
We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet.
Emotion Intensities in Tweets
This paper examines the task of detecting intensity of emotion from text.
Word Affect Intensities
We also compare the entries in AIL with the entries in the NRC VAD Lexicon, which has valence, arousal, and dominance (VAD) scores for 20K English words.
Stance and Sentiment in Tweets
However, a person may express the same stance towards a target by using negative or positive language.
Generating Music from Literature
We present a system, TransProse, that automatically generates musical pieces from text.
Generating Extractive Summaries of Scientific Paradigms
Researchers and scientists increasingly find themselves in the position of having to quickly understand large amounts of technical material.
Experiments with Three Approaches to Recognizing Lexical Entailment
Two general strategies for RLE have been proposed: One strategy is to manually construct an asymmetric similarity measure for context vectors (directional similarity) and another is to treat RLE as a problem of learning to recognize semantic relations using supervised machine learning techniques (relation classification).
Identifying Purpose Behind Electoral Tweets
Finally, we show that resources developed for emotion detection are also helpful for detecting purpose.
Using Nuances of Emotion to Identify Personality
Past work on personality detection has shown that frequency of lexical categories such as first person pronouns, past tense verbs, and sentiment words have significant correlations with personality traits.
Tracking Sentiment in Mail: How Genders Differ on Emotional Axes
With the widespread use of email, we now have access to unprecedented amounts of text that we ourselves have written.
Even the Abstract have Colour: Consensus in Word-Colour Associations
Colour is a key component in the successful dissemination of information.
NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets
In this paper, we describe how we created two state-of-the-art SVM classifiers, one to detect the sentiment of messages such as tweets and SMS (message-level task) and one to detect the sentiment of a term within a submissions stood first in both tasks on tweets, obtaining an F-score of 69. 02 in the message-level task and 88. 93 in the term-level task.
Computing Lexical Contrast
We then present an automatic and empirical measure of lexical contrast that relies on the contrast hypothesis, corpus statistics, and the structure of a {\it Roget}-like thesaurus.
Crowdsourcing a Word-Emotion Association Lexicon
Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons.
Distributional Measures of Semantic Distance: A Survey
The ability to mimic human notions of semantic distance has widespread applications.
