329 research outputs found
Word Affect Intensities
Words often convey affect -- emotions, feelings, and attitudes. Lexicons of
word-affect association have applications in automatic emotion analysis and
natural language generation. However, existing lexicons indicate only coarse
categories of affect association. Here, for the first time, we create an affect
intensity lexicon with real-valued scores of association. We use a technique
called best-worst scaling that improves annotation consistency and obtains
reliable fine-grained scores. The lexicon includes terms common from both
general English and terms specific to social media communications. It has close
to 6,000 entries for four basic emotions. We will be adding entries for other
affect dimensions shortly
The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition
Negators, modals, and degree adverbs can significantly affect the sentiment
of the words they modify. Often, their impact is modeled with simple
heuristics; although, recent work has shown that such heuristics do not capture
the true sentiment of multi-word phrases. We created a dataset of phrases that
include various negators, modals, and degree adverbs, as well as their
combinations. Both the phrases and their constituent content words were
annotated with real-valued scores of sentiment association. 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. We find that
the effect of modifiers varies substantially among the members of the same
group. Furthermore, each individual modifier can affect sentiment words in
different ways. Therefore, solutions based on statistical learning seem more
promising than fixed hand-crafted rules on the task of automatic sentiment
prediction.Comment: In Proceedings of the 7th Workshop on Computational Approaches to
Subjectivity, Sentiment and Social Media Analysis (WASSA), San Diego,
California, 201
Emotion intensities in Tweets
This paper examines the task of detecting intensity of emotion from text. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities. We use a technique called best–worst scaling (BWS) that improves annotation consistency and obtains reliable fine-grained scores. We show that emotion-word hashtags often impact emotion intensity, usually conveying a more intense emotion. Finally, we create a benchmark regression system and conduct experiments to determine: which features are useful for detecting emotion intensity; and, the extent to which two emotions are similar in terms of how they manifest in language
''Fifty Shades of Bias'': Normative Ratings of Gender Bias in GPT Generated English Text
Language serves as a powerful tool for the manifestation of societal belief
systems. In doing so, it also perpetuates the prevalent biases in our society.
Gender bias is one of the most pervasive biases in our society and is seen in
online and offline discourses. With LLMs increasingly gaining human-like
fluency in text generation, gaining a nuanced understanding of the biases these
systems can generate is imperative. Prior work often treats gender bias as a
binary classification task. However, acknowledging that bias must be perceived
at a relative scale; we investigate the generation and consequent receptivity
of manual annotators to bias of varying degrees. Specifically, we create the
first dataset of GPT-generated English text with normative ratings of gender
bias. Ratings were obtained using Best--Worst Scaling -- an efficient
comparative annotation framework. Next, we systematically analyze the variation
of themes of gender biases in the observed ranking and show that
identity-attack is most closely related to gender bias. Finally, we show the
performance of existing automated models trained on related concepts on our
dataset.Comment: Camera-ready version in EMNLP 202
Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words
Words play a central role in language and thought. Factor analysis studies have shown that the primary dimensions of meaning are valence, arousal, and dominance (VAD). We present the NRC VAD Lexicon, which has human ratings of valence, arousal, and dominance for more than 20,000 English words. We use Best–Worst Scaling to obtain fine-grained scores and address issues of annotation consistency that plague traditional rating scale methods of annotation. We show that the ratings obtained are vastly more reliable than those in existing lexicons. We also show that there exist statistically significant differences in the shared understanding of valence, arousal, and dominance across demographic variables such as age, gender, and personality
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. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best–worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language
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