Online reviews play a significant role in purchase decisions of consumers by providing feedback information from buyers of products. In order to mislead consumers, opinion spammers are hired to write fake reviews to promote or demote specific products for illegitimate benefits. Existing methods for spam review detection mainly focused on designing manual features, which highly rely on expert knowledge. Although recent works utilized deep learning methods to automatically learn the semantics of reviews through the inherent user-review-product strong relation, they fail to capture the weak relations between reviews at the content, sentiment and temporal levels, which provides various semantic information to expose fake reviews. Moreover, the imbalanced class distribution in spam detection issues makes this work even more challenging. To address the above problems, we propose a novel Weak-Strong Unified Network (WSUN) for opinion spam detection. Multi-level weak relation graphs are constructed to reveal the abnormal behavioral patterns of spammers, which aggregates the semantics of strong relations by graph convolutional networks and extracts comprehensive review representation by utilizing relation-level attention mechanism. In addition, a graph-based over-sampling method is devised to mitigate the impact of imbalanced class distribution. Extensive experimental results on real-world datasets show that our model is more effective than the state-of-the-art methods.

With the explosive growth of social media, opinionated postings with emojis have increased explosively. Many emojis are used to express emotions, attitudes, and opinions. Emoji representation learning can be helpful to improve the performance of emoji-related natural language processing tasks, especially in text sentiment analysis. However, most studies have only utilized the fixed descriptions provided by the Unicode Consortium without consideration of actual usage scenarios. As for the sentiment analysis task, many researchers ignore the emotional impact of the interaction between text and emojis. It results that the emotional semantics of emojis cannot be fully explored. In this work, we propose a method called EmoGraph2vec to learn emoji representations by constructing a co-occurrence graph network from social data and enriching the semantic information based on an external knowledge base EmojiNet to embed emoji nodes. Based on EmoGraph2vec model, we design a novel neural network to incorporate text and emoji information into sentiment analysis, which uses a hybrid-attention module combined with TextCNN-based classifier to improve performance. Experimental results show that the proposed model can outperform several baselines for sentiment analysis on benchmark datasets. Additionally, we conduct a series of ablation and comparison experiments to investigate the effectiveness and interpretability of our model.

Sentiment analysis is one of the key tasks of natural language understanding. Most of sentiment analysis researches revolve around sentiment classification of subjective texts. However, research in the field of sentiment evolution analysis for complex interactive texts are notable. Sentiment evolution models the dynamics of sentiment orientation over time, it can predict the stage of event development. In this paper, we propose a sentiment evolution method based on a joint model to analyze the dynamics and interactions of individual sentiment on social media such as Weibo. The model contains two modules, sentiment encoder module based on pre-training model and time series prediction module based on Long Short-Term Memory(LSTM). We conducted experiments on real-world datasets which were crawled from Weibo. The experiment demonstrated a case study that analyzed the sentiment dynamics of topics related to COVID-19. Experimental results show that our method achieve an accuracy of 88.0%, which are about 14.7% higher than the existing methods.