Research on the Application of Deep Learning inSentiment Analysis of Tourism Social Media Texts

With the rise of tourism social media, vast amounts of user-generated content have emerged, providing valuable insightsinto travel preferences, experiences, and sentiments. However, extracting meaningful patterns from such data remains achallenge due to the dynamic nature of social interactions, linguistic variability, and real-time engagement trends. Traditionalsentiment analysis methods, which largely rely on keyword matching and rule-based approaches, often fail to capture nuancedemotions and contextual meanings embedded in tourism-related discussions. Moreover, conventional machine learningmodels struggle with the evolving linguistic expressions and engagement fluctuations seen in social media platforms. Toaddress these limitations, this study proposes an advanced deep learning-based model, the Tourism-Oriented Social MediaAnalysis Model (TOSMAM), which integrates user interaction graphs, transformer-based textual representations, and temporalactivity modeling to enhance sentiment analysis in tourism social media. The model incorporates a self-adaptive attentionmechanism to dynamically adjust weight distribution based on engagement patterns, along with a reinforcement learning-basedoptimization strategy to improve predictive accuracy in sentiment classification. A personalized content filtering systemenhances relevance by adapting to user preferences, and a real-time anomaly detection module identifies emerging trends intourism discussions. Experimental evaluations demonstrate that TOSMAM significantly outperforms traditional sentimentanalysis techniques in accuracy, robustness, and real-time adaptability. The findings underscore the potential of deep learningin advancing sentiment analysis within tourism social media, providing a powerful tool for stakeholders to understand andrespond to traveler sentiments effectively