Emoji Prediction: A Transfer Learning Approach

Zhang, Linrui; Zhou, Yisheng; Erekhinskaya, Tatiana; Moldovan, Dan

doi:10.1007/978-3-030-39442-4_65

Cited by 6 publications

(2 citation statements)

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“…Using the Stanford Sentiment Treebank collection (SST-5) [32] as the training corpus, they showed that SentiBERT outperforms other neural models (Tree-LSTMs, GCN, RNNs) on a Twitter Collection. In [38], they developed an approach similar to SentiBERT, but trained on the collections provided by SemEval-2018 Task 1 organisers, and their approach outperformed existing baselines (such as SeerNet [5], SVM [20], PlusEmo2Vec [25], etc.). These results suggest that the BERT based regression model, SentiBERT, provides a state of the art supervised and transfer learning approach.…”

Section: Supervised Approachesmentioning

confidence: 99%

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Sentiment Intensity Prediction using Neural Word Embeddings

Htait

Azzopardi

2021

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval

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Sentiment analysis is central to the process of mining opinions and attitudes from online texts. While much attention has been paid to the sentiment classification problem, much less work has tried to tackle the problem of predicting the intensity of the sentiment. The go to method is VADER -an unsupervised lexicon based approach to scoring sentiment. However, such approaches are limited because of the vocabulary mismatch problem. In this paper, we present in detail and evaluate our AWESSOME framework (A Word Embedding Sentiment Scorer Of Many Emotions) [10] for sentiment intensity scoring, that capitalizes on pre-existing lexicons, does not require training and provides fine grained and accurate sentiment intensity scores of words, phrases and text. In our experiments, we used seven Sentiment Collections to evaluate the proposed approach, against lexicon based approaches (e.g., VADER), and supervised methods such as deep learning based approaches (e.g., SentiBERT). The results show that despite not surpassing supervised approaches, the AWESSOME unsupervised approach significantly outperforms existing lexicon approaches and therefore provides a simple and effective approach for sentiment analysis. The AWESSOME framework can be flexibly adapted to cater for different seed lexicons and different neural word embeddings models in order to produce corpus specific lexicons -without the need for extensive supervised learning or retraining.

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Section: Supervised Approachesmentioning

confidence: 99%

“…The second supervised method we employed was the BERTbased method: SentiBERT [37,38] which has been shown to give state of the art performance on one of the test collections (SE18-Vreg). SentiBERT is built on the HuggingFace 6 library, and the model parameters are initialized using pre-trained BERT-base model.…”

Section: Upperbounds: Supervised Approachesmentioning

confidence: 99%

Sentiment Intensity Prediction using Neural Word Embeddings

Htait

Azzopardi

2021

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval

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#let’s-discuss: Analyzing Students’ Use of Emoji when Interacting with Course Readings

Blobstein,

Facciotti,

Igo

et al. 2024

Int J Artif Intell Educ

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Emoji are commonly used in social media to convey affects, emotions, and attitudes. While popular in social media, their use in educational contexts has been sparsely studied even though emoji can be a natural way for students to express what they are feeling about the learning material. This paper studies how students use instructor-selected emoji when relating to and engaging with educational content. We use an online platform for collaborative annotations where discussions are embedded into the readings for the course. We also make it possible for students to use 11 unique emoji-hashtag pairings to express their thoughts and feelings about the readings and the ongoing discussion. We provide an empirical analysis of the usage of these emoji-hashtag pairs by over 1,800 students enrolled in different offerings of an introductory biology course from multiple academic terms. We also introduce a heat map, which allows the instructional team to visualize the distribution and types of emoji used by students in different parts of the reading material. To evaluate the heat map, we conducted a user study with five instructors/TAs. We found that instructors/TAs use the heat map as a tool for identifying textbook sections that students find difficult and/or interesting and plan to use it to help them design the online content for future classes. Finally, we introduce a computational analysis for predicting emoji/hashtag pairs based on the content of a given student post. We use pre-trained deep learning language models (BERT) to predict the emoji attached to a student’s post and then study the extent to which this model generated in an introductory biology course can be generalized to predict student emoji usage in other courses.

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Progress, achievements, and challenges in multimodal sentiment analysis using deep learning: A survey

Pandey,

Vishwakarma

2024

Applied Soft Computing

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Emoji Prediction: A Transfer Learning Approach

Cited by 6 publications

References 10 publications

Sentiment Intensity Prediction using Neural Word Embeddings

Sentiment Intensity Prediction using Neural Word Embeddings

#let’s-discuss: Analyzing Students’ Use of Emoji when Interacting with Course Readings

Progress, achievements, and challenges in multimodal sentiment analysis using deep learning: A survey

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