Sentiment Analysis Using Residual Learning with Simplified CNN Extractor Sentiment analysis has an important role in social media monitoring as it extracts public opinions, emotions, and feelings about certain products or services. There are several publications in building a system to identify opinions from text using rulebased approach, lexiconbased approach, or machine learning. In this paper, we propose and compare several deep learning models to solve sentiment analysis problem of the Internet Movie Database (IMDb) review sentiment dataset. The feature extractor consists of a convolutional layer, followed by a max pooling layer and a batch normalization layer. To solve the vanishing gradient problem, we use a residual connection to concatenate the input values with the extracted features before feeding the output into a recurrent layer. Our best model has an accuracy of 90.02%.

Sentiment strength detection with a contextdependent lexiconbased convolutional neural network Abstract   Sentiment strength detection is an essential task in sentiment analysis, wherein the sentiment strength of subjective text is automatically determined. Sentiment analysis has numerous applications in different sectors, including business and social domains. In this study, we present a model to effectively extract the features and strength of sentiment from words and text using a contextdependent, lexiconbased convolutional neural network. To build this convolutional neural network, the model is trained using the sentiment polarity for each word from a cooccurrence pattern of words and labels. Then, a contextdependent lexicon is generated from the corpus, which is used to generate positive and negative sentiment word embeddings. Positive sentiment word embeddings, negative sentiment word embeddings, and the pretrained word embeddings are input to a 3channel convolutional neural network (CNN) to predict the strength of the sentiments. Moreover, with the trained convolutional neural network model, we can obtain a learned sentiment strengthspecific word embedding, which generates a sentiment strengthspecific lexicon (SSSLex) that contains word associations and sentiment intensity scores. To validate the effectiveness of sentiment strength detection in the proposed model, we evaluate the model using six realworld datasets. Furthermore, to evaluate the sentiment strengthspecific lexicon, we compare it with seven existing lexicons in three evaluation tasks from the SemEval2015 and SemEval2016 competitions. Experimental results indicate that the proposed model can predict the sentiment strength of documents more effectively than the baseline methods, and that the SSSLex is of higher quality than the existing lexicons.