This paper proposes a neural network model for document-level sentiment classification using gated recurrent neural networks (GRNN) to encode sentence semantics and their relations in document representation. The model first learns sentence representations using convolutional neural networks (CNN) or long short-term memory (LSTM), then adaptively encodes sentence semantics and relations in document representations using GRNN. The model is trained end-to-end with stochastic gradient descent, using cross-entropy error as the loss function. The approach is evaluated on four large-scale review datasets from IMDB and Yelp Dataset Challenge, showing superior performance compared to state-of-the-art methods. Experimental results demonstrate that GRNN significantly outperforms standard recurrent neural networks in document modeling for sentiment classification. The main contributions include: (1) a neural network approach to encode sentence relations in document representation for sentiment classification; (2) empirical results on four large-scale datasets showing the approach outperforms state-of-the-art methods; and (3) empirical results showing that traditional RNN is weak in modeling document composition, while adding neural gates improves classification performance. The model is compared with baselines such as SVM, CNN, and paragraph vector, and shows significant improvements in accuracy and MSE. The approach is effective in capturing semantic relations between sentences, leading to better sentiment classification performance. The model is trained on four large-scale datasets, achieving state-of-the-art results. The paper also discusses related work, including previous methods for document-level sentiment classification and neural network approaches for semantic composition. The proposed model is effective in capturing document composition and semantic relations, leading to improved sentiment classification performance. The paper concludes that the proposed model achieves state-of-the-art performance on document-level sentiment classification tasks.This paper proposes a neural network model for document-level sentiment classification using gated recurrent neural networks (GRNN) to encode sentence semantics and their relations in document representation. The model first learns sentence representations using convolutional neural networks (CNN) or long short-term memory (LSTM), then adaptively encodes sentence semantics and relations in document representations using GRNN. The model is trained end-to-end with stochastic gradient descent, using cross-entropy error as the loss function. The approach is evaluated on four large-scale review datasets from IMDB and Yelp Dataset Challenge, showing superior performance compared to state-of-the-art methods. Experimental results demonstrate that GRNN significantly outperforms standard recurrent neural networks in document modeling for sentiment classification. The main contributions include: (1) a neural network approach to encode sentence relations in document representation for sentiment classification; (2) empirical results on four large-scale datasets showing the approach outperforms state-of-the-art methods; and (3) empirical results showing that traditional RNN is weak in modeling document composition, while adding neural gates improves classification performance. The model is compared with baselines such as SVM, CNN, and paragraph vector, and shows significant improvements in accuracy and MSE. The approach is effective in capturing semantic relations between sentences, leading to better sentiment classification performance. The model is trained on four large-scale datasets, achieving state-of-the-art results. The paper also discusses related work, including previous methods for document-level sentiment classification and neural network approaches for semantic composition. The proposed model is effective in capturing document composition and semantic relations, leading to improved sentiment classification performance. The paper concludes that the proposed model achieves state-of-the-art performance on document-level sentiment classification tasks.