{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bb_twtr-at-semeval-2017-task-4-twitter","title":"BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs","arxiv_id":"1704.06125","date":"2017-04-20","proceeding":"SEMEVAL 2017 8","authors":["Mathieu Cliche"],"abstract":"In this paper we describe our attempt at producing a state-of-the-art Twitter\nsentiment classifier using Convolutional Neural Networks (CNNs) and Long Short\nTerm Memory (LSTMs) networks. Our system leverages a large amount of unlabeled\ndata to pre-train word embeddings. We then use a subset of the unlabeled data\nto fine tune the embeddings using distant supervision. The final CNNs and LSTMs\nare trained on the SemEval-2017 Twitter dataset where the embeddings are fined\ntuned again. To boost performances we ensemble several CNNs and LSTMs together.\nOur approach achieved first rank on all of the five English subtasks amongst 40\nteams.","url_abs":"http://arxiv.org/abs/1704.06125v1","url_pdf":"http://arxiv.org/pdf/1704.06125v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bb_twtr-at-semeval-2017-task-4-twitter","repo_url":"https://github.com/kaliahinartem/twitter_sentiment_analysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"bb_twtr-at-semeval-2017-task-4-twitter","repo_url":"https://github.com/leelaylay/TweetSemEval","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"bb_twtr-at-semeval-2017-task-4-twitter","repo_url":"https://github.com/lopezbec/COVID19_Tweets_Dataset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"bb_twtr-at-semeval-2017-task-4-twitter","repo_url":"https://github.com/lopezbec/COVID19_Tweets_Dataset_2020","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"bb_twtr-at-semeval-2017-task-4-twitter","repo_url":"https://github.com/nileshsah/deep-text-classifier","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"bb_twtr-at-semeval-2017-task-4-twitter","repo_url":"https://github.com/saurabhrathor/InceptionModel_SentimentAnalysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"twitter-sentiment-analysis","task_name":"Twitter Sentiment Analysis"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"1d-cnn","method_name":"1D CNN"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sentiment-analysis-on-semeval","task":"Sentiment Analysis","dataset":"SemEval","model":"LSTMs+CNNs ensemble with multiple conv. ops","rank_in_archive_order":2,"of":2,"metrics":{"F1-score":"0.685"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-semeval-2017-task-4-a","task":"Sentiment Analysis","dataset":"SemEval 2017 Task 4-A","model":"LSTMs+CNNs ensemble with multiple conv. ops","rank_in_archive_order":1,"of":3,"metrics":{"Average Recall":"0.681"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1704.06125","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}