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.

Most implemented papers

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

lopezbec/COVID19_Tweets_Dataset SEMEVAL 2017

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 WS 2018

However, this dataset has not been comprehensively studied to its potential.

Decision Stream: Cultivating Deep Decision Trees

aiff22/Decision-Stream 25 Apr 2017

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

balikasg/sigir2017 12 Jul 2017

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 12 Mar 2019

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 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

tayebiarasteh/retweet 21 Apr 2021

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 - 2021

In this report, address the problem of sentiment classification on the Twitter dataset.

n-stage Latent Dirichlet Allocation: A Novel Approach for LDA

anil1055/n-stage_LDA 16 Oct 2021

In this article, the proposed n-stage LDA method, which can enable the LDA method to be used more effectively, is explained in detail.