Twitter sentiment analysis is the task of performing sentiment analysis on tweets from Twitter.
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.
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.
Ranked #1 on Sentiment Analysis on SemEval 2017 Task 4-A
Once we are happy with the quality of our input data, we proceed to choosing the optimal deep learning architecture for this task.
Traditional sentiment analysis approaches tackle problems like ternary (3-category) and fine-grained (5-category) classification by learning the tasks separately.
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.
Ranked #1 on Tweet-Reply Sentiment Analysis on RETWEET