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.
These leaderboards are used to track progress in Twitter Sentiment Analysis
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.
Traditional sentiment analysis approaches tackle problems like ternary (3-category) and fine-grained (5-category) classification by learning the tasks separately.
Once we are happy with the quality of our input data, we proceed to choosing the optimal deep learning architecture for this task.
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.
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.
In this article, the proposed n-stage LDA method, which can enable the LDA method to be used more effectively, is explained in detail.