This paper introduces a novel neural network model called C-LSTM for sentence representation and text classification. C-LSTM combines the strengths of convolutional neural networks (CNN) and long short-term memory networks (LSTM). The CNN is used to extract higher-level phrase representations from sentences, which are then fed into an LSTM to capture both local phrase features and global sentence semantics. The model is evaluated on sentiment classification and question classification tasks, where it outperforms both CNN and LSTM models. The C-LSTM model consists of two main components: a CNN for extracting phrase-level features and an LSTM for capturing long-term dependencies. The CNN processes the input sentence through convolutional filters to extract n-gram features, which are then passed to the LSTM. The LSTM processes these features to learn long-term dependencies and produce sentence representations. The model is trained using stochastic gradient descent with RMSProp optimization. The model is evaluated on two tasks: sentiment classification using the Stanford Sentiment Treebank (SST) dataset and question type classification using the TREC dataset. The results show that C-LSTM achieves excellent performance on both tasks, outperforming other models such as CNN, LSTM, and tree-structured models. The model is also compared with other baseline models, including recursive models, CNNs, and SVMs, and shows superior performance. The C-LSTM model is able to capture both local and global features of sentences, making it effective for text classification tasks. The model is trained on a large-scale dataset and uses pre-trained word vectors for initialization. The model is also regularized using dropout and L2 weight regularization to prevent overfitting. The experiments show that C-LSTM outperforms other models in both sentiment classification and question classification tasks. The model is able to learn semantic representations of sentences and capture the underlying structure of the input data. The results indicate that C-LSTM is a promising approach for text classification tasks and can be applied to various NLP applications.This paper introduces a novel neural network model called C-LSTM for sentence representation and text classification. C-LSTM combines the strengths of convolutional neural networks (CNN) and long short-term memory networks (LSTM). The CNN is used to extract higher-level phrase representations from sentences, which are then fed into an LSTM to capture both local phrase features and global sentence semantics. The model is evaluated on sentiment classification and question classification tasks, where it outperforms both CNN and LSTM models. The C-LSTM model consists of two main components: a CNN for extracting phrase-level features and an LSTM for capturing long-term dependencies. The CNN processes the input sentence through convolutional filters to extract n-gram features, which are then passed to the LSTM. The LSTM processes these features to learn long-term dependencies and produce sentence representations. The model is trained using stochastic gradient descent with RMSProp optimization. The model is evaluated on two tasks: sentiment classification using the Stanford Sentiment Treebank (SST) dataset and question type classification using the TREC dataset. The results show that C-LSTM achieves excellent performance on both tasks, outperforming other models such as CNN, LSTM, and tree-structured models. The model is also compared with other baseline models, including recursive models, CNNs, and SVMs, and shows superior performance. The C-LSTM model is able to capture both local and global features of sentences, making it effective for text classification tasks. The model is trained on a large-scale dataset and uses pre-trained word vectors for initialization. The model is also regularized using dropout and L2 weight regularization to prevent overfitting. The experiments show that C-LSTM outperforms other models in both sentiment classification and question classification tasks. The model is able to learn semantic representations of sentences and capture the underlying structure of the input data. The results indicate that C-LSTM is a promising approach for text classification tasks and can be applied to various NLP applications.