Machine Learning Sentiment Prediction based on Hybrid Document Representation
Table 2: Comparison of different methods on the MOVIES dataset. All results in this table, except for the last row, are taken from Kim (2014).
['Method', 'Test Accuracy', 'Reference']
Our methodology is assessed through the application of several classifiers on the MOVIES dataset that has been used extensively in literature for the evaluation of the various methodologies proposed for sentiment analysis. the authors report a series of experimental results using Convolutional Neural Networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. In Socher et al. a novel machine learning framework based on Recursive Autoencoders (RAE) for sentence-level prediction of sentiment label distributions, is introduced. The method supports learning vector space representations for multi-word phrases, without using any pre-defined sentiment lexica or polarity shifting rules. Also, in Socher et al. This model leverages the CCG combinatory operators to guide a nonlinear transformation of meaning within a sentence. They use this model to learn high dimensional embeddings for sentences and evaluate them in a range of tasks, demonstrating that the incorporation of syntax allows a concise model to learn representations that are both effective and general. In Wang and Manning the dropout training model Hinton et al. (zeroing) hidden units and input features during training of neural networks. This process speeds up training without losing classification performance. Nakagawa et al. Dong et al. Unlike previous works that employ syntactic parsing results for sentiment analysis, they develop a statistical parser to directly analyze the sentiment structure of a sentence.