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BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs

20 Apr 2017SEMEVAL 2017 8arXiv:1704.06125archive 2025-07-28

Mathieu Cliche

In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data to pre-train word embeddings. We then use a subset of the unlabeled data to fine tune the embeddings using distant supervision. The final CNNs and LSTMs are trained on the SemEval-2017 Twitter dataset where the embeddings are fined tuned again. To boost performances we ensemble several CNNs and LSTMs together. Our approach achieved first rank on all of the five English subtasks amongst 40 teams.

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Code

kaliahinartem/twitter_sentiment_analysis mentioned on GitHubpytorch report
leelaylay/TweetSemEval mentioned on GitHubpytorch report
lopezbec/COVID19_Tweets_Dataset mentioned on GitHub report
lopezbec/COVID19_Tweets_Dataset_2020 mentioned on GitHub report
nileshsah/deep-text-classifier mentioned on GitHub report
saurabhrathor/InceptionModel_SentimentAnalysis mentioned on GitHub report

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Tasks

Sentiment AnalysisTwitter Sentiment AnalysisWord Embeddings

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Sentiment Analysis SemEval LSTMs+CNNs ensemble with multiple conv. ops F1-score 0.685 #2 of 2 Archive leaderboard report
Sentiment Analysis SemEval 2017 Task 4-A LSTMs+CNNs ensemble with multiple conv. ops Average Recall 0.681 #1 of 3 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1D CNN

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