CSNN: Contextual Sentiment Neural Network Although deep neural networks are excellent for text sentiment analysis, their applications in realworld practice are occasionally limited owing to their blackbox property. In response, we propose a novel neural network model called contextual sentiment neural network (CSNN) model that can explain the process of its sentiment analysis prediction in a way that humans find natural and agreeable. The CSNN has the following interpretable layers: the wordlevel original sentiment layer, wordlevel sentiment shift layer, wordlevel local contextual sentiment layer, wordlevel global importance layer, and wordlevel global contextual sentiment layer. Because of these layers, this network can explain the process of its documentlevel sentiment analysis results in a humanlike way using these layers. Realizing the interpretability of each layer in the CSNN is a crucial problem in the development of this CSNN because the general backpropagation method cannot realize such interpretability. To realize this interpretability, we propose a novel learning strategy called initialization propagation (IP) learning. Using real textual datasets, we experimentally demonstrate that the proposed IP learning is effective for improving the interpretability of each layer in CSNN. We then experimentally demonstrate that both the predictability and explanation ability of the CSNN are high.

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.