Twitter Sentiment Analysis
10 papers with code • 0 benchmarks • 2 datasets
Twitter sentiment analysis is the task of performing sentiment analysis on tweets from Twitter.
Benchmarks
These leaderboards are used to track progress in Twitter Sentiment Analysis
RETWEET
Twitter Sentiment Analysis
Most implemented papers
BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs
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.
Comparative Studies of Detecting Abusive Language on Twitter
younggns/comparative-abusive-lang
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WS 2018
However, this dataset has not been comprehensively studied to its potential.
NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis
The first space is a bag-of-words model and has a Linear SVM as base classifier.
Decision Stream: Cultivating Deep Decision Trees
Various modifications of decision trees have been extensively used during the past years due to their high efficiency and interpretability.
Multitask Learning for Fine-Grained Twitter Sentiment Analysis
Traditional sentiment analysis approaches tackle problems like ternary (3-category) and fine-grained (5-category) classification by learning the tasks separately.
Offensive Language Analysis using Deep Learning Architecture
RyanOngAI/semeval-2019-task6
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Once we are happy with the quality of our input data, we proceed to choosing the optimal deep learning architecture for this task.
GRUBERT: A GRU-Based Method to Fuse BERT Hidden Layers for Twitter Sentiment Analysis
zuowenwang0000/grubert-a-gru-based-method-to-fuse-bert-hidden-layers
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Asian Chapter of the Association for Computational Linguistics 2020
In this work, we introduce a GRU-based architecture called GRUBERT that learns to map the different BERT hidden layers to fused embeddings with the aim of achieving high accuracy on the Twitter sentiment analysis task.
How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies
As a strong baseline, we propose a two-stage DL-based method: first, we create automatically labeled training data by applying a standard sentiment classifier to tweet replies and aggregating its predictions for each original tweet; our rationale is that individual errors made by the classifier are likely to cancel out in the aggregation step.
Twitter Sentiment Analysis
Vedurumudi-Priyanka/Twitter-Sentiment-Analysis
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- 2021
In this report, address the problem of sentiment classification on the Twitter dataset.
n-stage Latent Dirichlet Allocation: A Novel Approach for LDA
In this article, the proposed n-stage LDA method, which can enable the LDA method to be used more effectively, is explained in detail.
