About

Twitter sentiment analysis is the task of performing sentiment analysis on tweets from Twitter.

Benchmarks

You can find evaluation results in the subtasks. You can also submitting evaluation metrics for this task.

Subtasks

Datasets

Greatest papers with code

Comparative Studies of Detecting Abusive Language on Twitter

WS 2018 JackonYang/maya

In this paper, we conduct the first comparative study of various learning models on Hate and Abusive Speech on Twitter, and discuss the possibility of using additional features and context data for improvements.

ABUSIVE LANGUAGE HATE SPEECH DETECTION TWITTER SENTIMENT ANALYSIS

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

SEMEVAL 2017 lopezbec/COVID19_Tweets_Dataset

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.

4 TWITTER SENTIMENT ANALYSIS WORD EMBEDDINGS

Decision Stream: Cultivating Deep Decision Trees

25 Apr 2017aiff22/Decision-Stream

Various modifications of decision trees have been extensively used during the past years due to their high efficiency and interpretability.

FEATURE SELECTION IMAGE CLASSIFICATION TWITTER SENTIMENT ANALYSIS

Offensive Language Analysis using Deep Learning Architecture

12 Mar 2019RyanOngAI/semeval-2019-task6

Once we are happy with the quality of our input data, we proceed to choosing the optimal deep learning architecture for this task.

ABUSE DETECTION TWITTER SENTIMENT ANALYSIS

Multitask Learning for Fine-Grained Twitter Sentiment Analysis

12 Jul 2017balikasg/sigir2017

Traditional sentiment analysis approaches tackle problems like ternary (3-category) and fine-grained (5-category) classification by learning the tasks separately.

TWITTER SENTIMENT ANALYSIS

How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies

3 Mar 2021starasteh/retweet

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.

TWEET-REPLY SENTIMENT ANALYSIS