| Challenge: | Tweets are short messages that often include specialized language such as hashtags and emojis. |
| Approach: | They propose a simple strategy to replace emojis with their natural language description and use pretrained word embeddings to process tweets. |
| Outcome: | The proposed method is more effective than pretrained emoji embeddings for tweet classification. |
Similar Papers
TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Modern NLP systems are typically ill-equipped when applied to noisy user-generated text. |
| Approach: | They propose a new evaluation framework consisting of seven Twitter-specific classification tasks. |
| Outcome: | The proposed framework is based on seven heterogeneous Twitter-specific classification tasks. |
Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Training (2024.emnlp-main)
Copied to clipboard
| Challenge: | Emojis have gained immense popularity on social media platforms, serving as a common means to supplement or replace text. |
| Approach: | They propose a graph pre-train framework for text and emoji co-modeling that incorporates two tasks: node-level graph contrastive learning and edge-level link reconstruction learning. |
| Outcome: | The proposed framework improves on the Xiaohongshu and Twitter datasets with two types of downstream tasks. |
Assessing Emoji Use in Modern Text Processing Tools (2021.acl-long)
Copied to clipboard
| Challenge: | Emojis are textual elements that are encoded as characters but rendered as small digital images or icons that can be used to express an idea or emotion. |
| Approach: | They propose to use a set of popular NLP tools to assess the support of emojis in tweets. |
| Outcome: | The proposed methods show that many systems still have notable shortcomings when operating on text containing emojis. |
Classifying the Informative Behaviour of Emoji in Microblogs (L18-1)
Copied to clipboard
| Challenge: | Emoji are pictographs used in microblogs as emotion markers, but can also represent a wider range of concepts. |
| Approach: | They analyze a corpus of tweets pairs and classify emoji with respect to redundancy . they propose to further investigate the informative behaviour of e-mails using eoji . |
| Outcome: | The proposed model achieved an F-score of 0.7 for emoji use in 2475 tweets pairs. |
What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection (D19-55)
Copied to clipboard
| Challenge: | Existing datasets for irony detection only contain 10% of ironic tweets with emojis . 45% of internet users in the united states use an e-moji in social media . |
| Approach: | They propose to use emojis to analyze irony detection datasets to train classifiers. |
| Outcome: | The proposed pipeline can be used to analyze irony detection datasets using emojis. |
Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings (2021.emnlp-main)
Copied to clipboard
| Challenge: | Semantic sentence embeddings are usually supervisedly built minimizing distances between pairs of embeddable sentences labelled as semantically similar by annotators. |
| Approach: | They propose a language-independent approach to build large datasets of pairs of informal texts weakly similar, without manual human effort, exploiting Twitter’s powerful signals of relatedness: replies and quotes of tweets. |
| Outcome: | The proposed model learns classical Semantic Textual Similarity, and excels on tasks where pairs of sentences are not exact paraphrases. |
Extracting Possessions from Social Media: Images Complement Language (D19-1)
Copied to clipboard
| Challenge: | Existing studies show that authors of tweets possess objects they tweet about. |
| Approach: | They propose a dataset and experiments to determine whether tweet authors possess objects they tweet about. |
| Outcome: | The proposed strategy incorporates visual information into any neural network beyond weights from pretrained networks. |
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing models for sentiment analysis over tweets require a substantial amount of text to adapt to a domain where the syntax is different. |
| Approach: | They propose to use a multilingual transformer model to train over tweets in five different languages to adapt the model to non-English languages. |
| Outcome: | The proposed model improves over small corpora of tweets in non-English languages. |
Interpretable Emoji Prediction via Label-Wise Attention LSTMs (D18-1)
Copied to clipboard
| Challenge: | Emojis are the evolution of characterbased emoticons and are used to express ideas about a myriad of topics. |
| Approach: | They propose a label-wise attention mechanism to better understand emoji prediction . they propose to model e-mails with eojis and then label them based on their meaning . |
| Outcome: | The proposed model improves over baselines and does particularly well when predicting infrequent emojis. |
Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition (N19-1)
Copied to clipboard
| Challenge: | Existing methods to analyze tweets are based on lexical features and a multi-channel convolutional neural architecture. |
| Approach: | They propose a neural network which can use different emotion and sentiment indicators such as hashtags, emoticons and emojis present in tweets to improve the performance of emotion and feelings identification. |
| Outcome: | The proposed model can use hashtags, emoticons and emojis present in tweets and improves emotion and sentiment identification. |