An LSTM model for Twitter Sentiment Analysis

TL;DR

This paper develops an LSTM-based model for classifying sentiment in Twitter data, utilizing a new combined dataset for training and evaluation to improve sentiment analysis accuracy.

Contribution

The paper introduces a new dataset created from multiple sources and applies an LSTM model specifically for Twitter sentiment classification.

Findings

LSTM model effectively classifies Twitter sentiment

New combined dataset improves evaluation robustness

Model achieves promising accuracy on Twitter sentiment data

Abstract

Sentiment analysis on social media such as Twitter provides organizations and individuals an effective way to monitor public emotions towards them and their competitors. As a result, sentiment analysis has become an important and challenging task. In this work, we have collected seven publicly available and manually annotated twitter sentiment datasets. We create a new training and testing dataset from the collected datasets. We develop an LSTM model to classify sentiment of a tweet and evaluate the model with the new dataset.