Sentimental analysis of social networking data is an economically affordable and effective way to track and evaluate public viewpoints that are critical for decision making in different areas. Predicting the users’ future opinions is crucial for companies and services; if companies understand users’ sentiments in considered time frames, they can do much better by knowing where exactly users are satisfied or unsatisfied. Utilizing an auxiliary dataset, this study uses the opinions of users on the Twitter social network expressed in the form of short text, and presents the Auxiliary Dataset-Latent Dirichlet Allocation (AD-LDA) model to improve the learning of users’ emotions around a specific topic. The proposed model considers the emotions –as predefined sentiments with a wide sentimental outlook– to estimate users’ feelings and sentiments about a particular subject or event. Coherence score evaluation results for the four studied hashtags showed an average 64.15% improvement compared to the conventional LDA model. The average Weighted-F1 criteria for studied hashtags was 79.83% for the accuracy of learning. Experimental and evaluation results show that our proposed model can effectively learn the emotions of words which leads to a better understanding of users’ feelings.

对社交网络数据进行情感分析是跟踪和评估对于不同领域的决策至关重要的公众观点的经济实惠且有效的方式。预测用户的未来意见对于公司和服务至关重要。如果公司在考虑的时间范围内了解用户的情绪，则可以通过了解用户确切满意或不满意的地方来做得更好。利用辅助数据集，本研究使用用户在Twitter社交网络上以短文本形式表达的意见，并提出了辅助数据集-潜在狄利克雷分配（AD-LDA）模型，以提高用户围绕社交网络的情感学习。具体主题。所提出的模型将情感视为具有广泛情感前景的预定义情感，以估计用户对特定主题或事件的感觉和情感。与传统的LDA模型相比，四个研究的标签的相干得分评估结果显示平均提高了64.15％。对于学习的标签，加权加权F1的平均标准对于学习的准确性为79.83％。实验和评估结果表明，我们提出的模型可以有效地学习单词的情感，从而更好地理解用户的感受。