/Sentiment-Analysis-with-RNN-and-CNN

Project of SJTU-CS438 Internet-based Information Extraction Technologies

Primary LanguagePythonMIT LicenseMIT

Sentiment-Analysis

Project of SJTU-CS438 Internet-based Information Extraction Technologies

For a given comment on anything included in the train corpus, the model predicts its polarity (positive/negative) with an architecture that first converts the words into embedding vectors, then passes them through a neural network combining LSTM and convolutional layers to achieve better performance.

Dataset

We collected and annoted a dataset containing 34639 Chinese comments and 13385 English comments on movies, books, music and electronic devices, which can be found in the dataset folder.

Precision

  • Chinese: 87.47%

  • English: 83.69%

  • You may use your own word embedding and train the model with a new language

Examples

  • The cake is a lie. Negative

  • I'm lovin' it. Positive

Team members: