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Although the state‐of‐the‐art sentiment classification approaches, such as LSTM and TextCNN, have achieved a good performance on Chinese short text sentiment analysis, the Chinese long text sentiment classification is still a challenge because of the sentiment change problem and the long text structure problem. Therefore, we propose a grammar guided embedding model (GGE) and a novel Chinese long text sentiment classification framework. First, the part‐of‐speech (POS) tags are introduced as the Chinese long text grammar guided information which can help classification approaches to model the Chinese long text structure and the important structure of sentiment change. Second, we proposed a simple GGE training method which considers the combination representation of word sequence and POS sequence. Finally, we proposed a unified framework which combines our novel GGE with TextCNN. Experiment results show that after using GGE, the model outperforms the state‐of‐the‐art approaches. At the same time, we also found that the GGE achieves the model converge faster, that is, it can achieve better results than without GGE when there is only a small amount of training data. Thus, we believe that the GGE can help machines better understand human language sentiment expression structure. [ABSTRACT FROM AUTHOR]

Understanding the sentiments of users from cross media contents which contain texts and images is an important task for many social network applications. However, due to the semantic gap between cross media features and sentiments, machine learning methods need a lot of human labeled samples. Furthermore, for each kind of media content, it is necessary to constantly add a lot of new human labeled samples because of new expressions of sentiments. Fortunately, there are some emotion signals, like emoticons, which denote users’ emotions in cross media contents. In order to use these weakly labels to build a unified multi-modality sentiment learning framework, we propose an Explicit Emotion Signal (EES) based multi-modality sentiment learning approach which uses huge number of weakly labeled samples in sentiment learning. There are three advantages in our approach. Firstly, only a few human labeled samples are needed to reach the same performance which can be obtained by the traditional machine learning based sentiment prediction approaches. Secondly, this approach is flexible and can easily combine text and vision based sentiment learning through deep neural networks. Thirdly, because a lot of weakly labeled samples can be used in EES, trained model is more robust in different domain transfer. In this paper, firstly, we investigate the correlation between sentiments and emoticons and choose emoticons as the Explicit Emotion Signals in our approach; secondly, we build a two stages multi-modality sentiment learning framework based on Explicit Emotion Signals. Our experiment results show that our approach not only achieves the best performance but also only needs 3% and 43% training samples to obtain the same performance of Visual Geometry Group (VGG) model and Long Short-Term Memory (LSTM) model in images and texts, respectively. [ABSTRACT FROM AUTHOR]

With a growing number of images being used to express opinions in Microblog, text based sentiment analysis is not enough to understand the sentiments of users. To obtain the sentiments implied in Microblog images, we propose a Visual Sentiment Topic Model (VSTM) which gathers images in the same Microblog topic to enhance the visual sentiment analysis results. First, we obtain the visual sentiment features by using Visual Sentiment Ontology (VSO); then, we build a Visual Sentiment Topic Model by using all images in the same topic; finally, we choose better visual sentiment features according to the visual sentiment features distribution in a topic. The best advantage of our approach is that the discriminative visual sentiment ontology features are selected according to the sentiment topic model. The experiment results show that the performance of our approach is better than VSO based model. [ABSTRACT FROM AUTHOR]

Since classical public sentiment analysis systems for microblog are based on the text sentiment analysis, it is difficult to determine the sentiment of short text without clear sentiment words in microblog posts. Fortunately, a lot of microblog posts contain images which also represent users' sentiment. To fully understand users' sentiment, we propose a cross-media public sentiment analysis system for microblog. The best advantage of this novel system is the unified cross-media public sentiment analysis framework which fuses the text sentiment and image sentiment not only from sentiment results, but also from sentiment ontology. To enhance presentation effects, this system presents sentiment results from macroscopic view and microscopic view which details the sentiment results in region, topic, microblog content and user diffusion. In our knowledge, this is the first unified cross-media public sentiment analysis system. [ABSTRACT FROM AUTHOR]