CancerEmo: A Dataset for Fine-Grained Emotion Detection

Sosea, Tiberiu; Caragea, Cornelia

doi:10.18653/v1/2020.emnlp-main.715

Cited by 22 publications

(18 citation statements)

References 34 publications

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“…As reference, we evaluate also content-based attribute classification using the tweets posted by each user as relevant evidence. In this case, the tweets authored by each user, t u , are first converted into a 300-dimension text embedding vector using the pre-trained convolutional FastText neural model (Joulin et al 2017), 9 which is often used for tweet processing (Sosea and Caragea 2020). We average the Fast-Text tweet embeddings (Adi et al 2017), and feed the resulting user representation to the logistic regression network.…”

Section: Methodsmentioning

confidence: 99%

SocialVec: Social Entity Embeddings

Lotan¹,

Minkov²

2021

Preprint

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This paper introduces SocialVec, a general framework for eliciting social world knowledge from social networks, and applies this framework to Twitter. SocialVec learns lowdimensional embeddings of popular accounts, which represent entities of general interest, based on their co-occurrences patterns within the accounts followed by individual users, thus modeling entity similarity in socio-demographic terms. Similar to word embeddings, which facilitate tasks that involve text processing, we expect social entity embeddings to benefit tasks of social flavor. We have learned social embeddings for roughly 200,000 popular accounts from a sample of the Twitter network that includes more than 1.3 million users and the accounts that they follow, and evaluate the resulting embeddings on two different tasks. The first task involves the automatic inference of personal traits of users from their social media profiles. In another study, we exploit SocialVec embeddings for gauging the political bias of news sources in Twitter. In both cases, we prove SocialVec embeddings to be advantageous compared with existing entity embedding schemes. We will make the SocialVec entity embeddings publicly available to support further exploration of social world knowledge as reflected in Twitter.

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Section: Methodsmentioning

confidence: 99%

SocialVec: Social Entity Embeddings

Lotan¹,

Minkov²

2021

Preprint

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“…These votes are then replaced with new votes by asking the questions from other individuals. A similar method of labeling was used by (Sosea and Caragea, 2020) and was shown to obtain satisfactory results. However, it is important to keep in mind that the perception of emotions can be quite subjective.…”

Section: Labelingmentioning

confidence: 95%

“…(Gollapalli et al, 2020) introduces an unsu-pervised emotion detection method which is built upon word co-occurrences and word associations. Some emotion datasets include: (Sosea and Caragea, 2020) from an English online health community with a focus on cancer, (Demszky et al, 2020) from English Reddit comments, from long-form narratives in English, (Kumar et al, 2019) from Hindi stories, and (Almahdawi and Teahan, 2019) an Arabic dataset from Facebook posts written in the Iraqi dialect. While (Khosravi et al, 2019) uses machine learning models to detect emotions of Persian news texts, the dataset has not been published.…”

Section: Related Workmentioning

confidence: 99%

EmoPars: A Collection of 30K Emotion-Annotated Persian Social Media Texts

Sabri¹,

Akhavan²

2021

Proceedings of the Student Research Workshop Associated With RANLP 2021

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The wide reach of social media platforms, such as Twitter, have enabled many users to share their thoughts, opinions and emotions on various topics online. The ability to detect these emotions automatically would allow social scientists, as well as, businesses to better understand responses from nations and costumers. In this study we introduce a dataset of 30,000 Persian Tweets labeled with Ekman's six basic emotions (Anger, Fear, Happiness, Sadness, Hatred, and Wonder). This is the first publicly available emotion dataset in the Persian language. In this paper, we explain the data collection and labeling scheme used for the creation of this dataset. We also analyze the created dataset, showing the different features and characteristics of the data. Among other things, we investigate co-occurrence of different emotions in the dataset, and the relationship between sentiment and emotion of textual instances. The dataset is publicly available at https://github.com/nazaninsb r/Persian-Emotion-Detection.

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“…Stanford Sentiment Treebank (SST) (Socher et al, 2013) CancerEmo (Sosea and Caragea, 2020) is a sentence-level multilabel dataset of 8, 500 sentences labeled with the eight Plutchik (Plutchik, 1980) basic emotions from an Online Health Community for people suffering from diseases such as cancer.…”

Section: Datasets and Lexiconsmentioning

confidence: 99%

“…There are numerous studies that focus on emotion detection (Demszky et al, 2020;Desai et al, 2020;del Arco et al, 2020;Sosea and Caragea, 2020;Majumder et al, 2019;Mohammad and Kiritchenko, 2018;Abdul-Mageed and Ungar, 2017;Mohammad and Kiritchenko, 2015;Mohammad, 2012;Strapparava and Mihalcea, 2008) and sentiment analysis (Yin et al, 2020;Tian et al, 2020;Phan and Ogunbona, 2020;Zhai and Zhang, 2016;Chen et al, 2016;Liu, 2012;Glorot et al, 2011;Pang and Lee, 2005). Various lexicons have been used to improve model performance on these tasks.…”

Section: Introductionmentioning

confidence: 99%

eMLM: A New Pre-training Objective for Emotion Related Tasks

Sosea¹,

Caragea²

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Bidirectional Encoder Representations from Transformers (BERT) have been shown to be extremely effective on a wide variety of natural language processing tasks, including sentiment analysis and emotion detection. However, the proposed pre-training objectives of BERT do not induce any sentiment or emotion-specific biases into the model. In this paper, we present Emotion Masked Language Modeling, a variation of Masked Language Modeling, aimed at improving the BERT language representation model for emotion detection and sentiment analysis tasks. Using the same pre-training corpora as the original BERT model, Wikipedia and BookCorpus, our BERT variation manages to improve the downstream performance on 4 tasks for emotion detection and sentiment analysis by an average of 1.2% F1. Moreover, our approach shows an increased performance in our task-specific robustness tests. We make our code and pre-trained model available at https://github.com/tsosea2/eMLM.

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CancerEmo: A Dataset for Fine-Grained Emotion Detection

Cited by 22 publications

References 34 publications

SocialVec: Social Entity Embeddings

SocialVec: Social Entity Embeddings

EmoPars: A Collection of 30K Emotion-Annotated Persian Social Media Texts

eMLM: A New Pre-training Objective for Emotion Related Tasks

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