Decoding sentiment: A sentiment analysis model for movie reviews

Abstract

Sentiment analysis of movie reviews can provide valuable insights into movie reactions and preferences. To this end, this study proposes the Convolutional Long Short-Term Memory (ConvLSTM) neural network for movie review sentiment analysis. ConvLSTM can efficiently capture sequential information due to its recurrent neural network characteristics. Specifically, the movie review data are first tokenized. Next, the ConvLSTM analysis model is constructed additionally by fine-tuning its parameters to optimize the performance. The ConvLSTM model consists of multiple storage units that retain contextual information, enabling the model to identify long-distance dependencies in the text. The network is trained using a combination of positive and negative movie reviews, and the training process involves adjusting the model weights to minimize the classification error. Experimental results demonstrate the effectiveness of the proposed method in accurately predicting movie review sentiment. It outperforms traditional machine learning methods in sentiment analysis tasks. The findings demonstrate the potential of LSTM-based sentiment analysis in various applications such as movie recommendation systems and market research. This study's findings help advance the development of sentiment analysis techniques and are of great relevance in understanding and catering to audience preferences in the movie industry.