An LSTM model for Twitter Sentiment Analysis
This work addresses sentiment analysis for organizations and individuals monitoring public emotions on social media, but it is incremental as it applies an existing method to new data.
The authors tackled sentiment analysis on Twitter by developing an LSTM model and evaluating it on a new dataset created from seven publicly available annotated datasets, achieving results that are competitive with existing benchmarks.
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.