# An LSTM model for Twitter Sentiment Analysis

arXiv: https://arxiv.org/abs/2212.01791 · HTML: https://mlsanity.com/paper/2212.01791/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 4 Dec 2022.

## Builds on (most cited first, 1 of 1)

- [SemEval-2014 Task 9: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02990/) (2019)

## Built on by (most cited first, 1 of 1)

- [Unveiling Public Opinion: A Study of Sentiment Analysis Using LSTM and Traditional Models](https://mlsanity.com/paper/2607.07772/) (2026)

## Abstract (arXiv)

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.