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

2017-04-20 · SEMEVAL 2017 8 · 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.

📄 PDF Abstract BibTeX arXiv:1704.06125

Code (6)

kaliahinartem/twitter_sentiment_analysis pytorch

leelaylay/TweetSemEval pytorch

lopezbec/COVID19_Tweets_Dataset

lopezbec/COVID19_Tweets_Dataset_2020

nileshsah/deep-text-classifier

saurabhrathor/InceptionModel_SentimentAnalysis

Tasks

Sentiment AnalysisTwitter Sentiment AnalysisWord Embeddings

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