Sentiment Analysis with LSTM: Experiment Summary using Sentiment140 Dataset

This experiment implemented sentiment analysis using an LSTM model and the Sentiment140 dataset. The dataset underwent preprocessing, converting text into word vector representations and applying padding. The LSTM model was then trained and used for prediction.

Exploratory data analysis revealed an imbalance in the sentiment polarity distribution within the dataset, with significantly more positive sentiment data than negative. To mitigate overfitting towards positive sentiment, a weighted cross-entropy loss function was employed during training.

An LSTM model was constructed, trained, and evaluated. The results indicated a successful performance on the test set, achieving an accuracy of 83%. This demonstrated the effectiveness of LSTM in sentiment analysis.

The analysis revealed a stronger performance in predicting positive sentiment compared to negative. This discrepancy might be attributed to the larger amount of positive sentiment data in the dataset, potentially leading to a bias towards positive predictions. To enhance the model's performance, considering a larger dataset or exploring alternative model architectures could be beneficial.

In conclusion, this experiment demonstrated the effectiveness of LSTM in sentiment analysis using the Sentiment140 dataset. The results highlight the importance of addressing data imbalance and carefully selecting model structures for optimal performance in deep learning-based sentiment analysis.