To exactly classify sentiments of microblog reviews with emojis in microblog social networks, this paper first proposes an emoji vectorisation method to achieve emoji vectors. Then, an emoji-text integrated bidirectional LSTM (ET-BiLSTM) model for sentiment analysis is proposed. In this model, review text-based sentence representations are extracted by a bidirectional LSTM network. Emoji-based auxiliary representations are obtained by a new attention mechanism. The two representations are further integrated into final review representation vectors. Finally, experimental results indicate that the proposed ET-BiLSTM model improves the performance of sentiment classification evaluated by macro-P, macro-R and macro-F1 scores in microblog social networks.

Recent advancements in online social networks (e.g., Facebook, Twitter, Weibo) highlight the need for effective interventions in combating the rapid spread of negative emotions triggered by adverse public events. Existing studies on emotional contagion have explored factors like network structure and individual susceptibility but lack a systematic understanding of intervention measures and their efficacy. This article addresses this gap as the negative emotion intervention (NEI) problem. While current strategies, such as blocking users, struggle with accurately interpreting subtle shifts in contagion patterns, our study focuses on leveraging reinforcement learning with deep Q-networks (DQN) to develop dynamic blocking strategies. We introduce the novel negative emotion contagion model (NECM) that considers spreaders’ capability, receivers’ sensitivity, and event popularity. Our study pioneers the optimized dynamic blocking approach, NECM-RLDB, which outperforms heuristic methods (out-degree, betweenness centrality, PageRank) in experimental tests on artificial and real-world datasets.