A Survey of Sentiment Analysis Based on Transfer Learning <s> 2) RNN-BASED MODELS <s> Document level sentiment classification remains a challenge: encoding the intrinsic relations between sentences in the semantic meaning of a document. To address this, we introduce a neural network model to learn vector-based document representation in a unified, bottom-up fashion. The model first learns sentence representation with convolutional neural network or long short-term memory. Afterwards, semantics of sentences and their relations are adaptively encoded in document representation with gated recurrent neural network. We conduct document level sentiment classification on four large-scale review datasets from IMDB and Yelp Dataset Challenge. Experimental results show that: (1) our neural model shows superior performances over several state-of-the-art algorithms; (2) gated recurrent neural network dramatically outperforms standard recurrent neural network in document modeling for sentiment classification. 1 <s> BIB001 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> 2) RNN-BASED MODELS <s> One-hot CNN (convolutional neural network) has been shown to be effective for text categorization (Johnson&Zhang, 2015). We view it as a special case of a general framework which jointly trains a linear model with a non-linear feature generator consisting of `text region embedding + pooling'. Under this framework, we explore a more sophisticated region embedding method using Long Short-Term Memory (LSTM). LSTM can embed text regions of variable (and possibly large) sizes, whereas the region size needs to be fixed in a CNN. We seek effective and efficient use of LSTM for this purpose in the supervised and semi-supervised settings. The best results were obtained by combining region embeddings in the form of LSTM and convolution layers trained on unlabeled data. The results indicate that on this task, embeddings of text regions, which can convey complex concepts, are more useful than embeddings of single words in isolation. We report performances exceeding the previous best results on four benchmark datasets. <s> BIB002 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> 2) RNN-BASED MODELS <s> In recent years, there has been an increasing interest in sentiment analysis on consumer reviews to understand the opinion polarity on social media. However, little attention has been paid to the development of deep learning for sentiment analysis on consumer reviews in Chinese. The research objective of this paper is to explore the impact of deep learning for sentiment analysis on Google Play consumer reviews in Chinese. A web mining technique was implemented for collecting 196,651 reviews on Google Play. We used Long Short Term Memory (LSTM) deep learning model, Naive Bayes (NB), and support vector machine (SVM) approaches for sentiment analysis on consumer reviews and compared the experimental results. The experimental results suggest that the accuracy of deep learning for sentiment analysis on Google Play consumer review achieves 94% and deep learning approach outperforms Naive Bayes (74.12%) and Support Vector Machine (76.46%) in the present study. Our finding confirmed that sentiment analysis on Google Play consumer review with deep learning is outstanding. The contributions of this paper are three-fold. First, the present study confirmed sentiment analysis with deep learning on Google Play consumer review may improve the accuracy of prediction. Second, we create a sentiment dictionary named iSGoPaSD for Google Play review. Third, the study compared the result of average sampling data and non-average sampling data. We found that deep learning method with non-average sampling data reached the better performance. <s> BIB003
Compared with the CNN model, Recurrent Neural Network (RNN) is more widely used in the NLP field, because RNN can process sequence information, and it has advantages in positional relationships, dependencies, etc., so it is more suitable for processing text information. Tang et al. BIB001 used a bottom-up method to learn vectorbased document representation. First, use CNN or Long Short-Term Memory (LSTM) to implement single-sentence representation, and then use GRU (Gated Recurrent Unit) network to encode the intrinsic and semantic connections between sentences. By this method, semantic information between sentences can be captured easily and better analysis results can be achieved. Day and Lin BIB003 introduced a method for text analysis of Chinese Google Play consumer reviews using the LSTM deep learning model, achieving better performance in comparison with the Naive Bayes method and the support vector machine method. Johnson and Zhang BIB002 explored a more complex LSTM region embedding method. LSTM can embed variablesized text regions and achieve optimal results by combining LSTM-style region embedding and convolutional layers. RNN can effectively integrate the information of the adjacent position of the text. However, the principle of the RNN model itself has some limitations, for example, it cannot distinguish the importance of word context cues, so it is necessary to explore a hybrid system of various model mechanisms to make up for its own shortcomings.