In recent years, sentiment analysis and emotion classification are two of the most abundantly used techniques in the field of Natural Language Processing (NLP). Although sentiment analysis and emotion classification are used commonly in applications such as analyzing customer reviews, the popularity of candidates contesting in elections, and comments about various sporting events; however, in this study, we have examined their application for epidemic outbreak detection. Early outbreak detection is the key to deal with epidemics effectively, however, the traditional ways of outbreak detection are time-consuming which inhibits prompt response from the respective departments. Social media platforms such as Twitter, Facebook, Instagram, etc. allow the users to express their thoughts related to different aspects of life, and therefore, serve as a substantial source of information in such situations. The proposed study exploits the bilingual (Urdu and English) data from Twitter and NEWS websites related to the dengue epidemic in Pakistan, and sentiment analysis and emotion classification are performed to acquire deep insights from the data set for gaining a fair idea related to an epidemic outbreak. Machine learning and deep learning algorithms have been used to train and implement the models for the execution of both tasks. The comparative performance of each model has been evaluated using accuracy, precision, recall, and f1-measure.
翻译:近年来,情绪分析和情绪分类是自然语言处理(NLP)领域最常用的两种技术。尽管情绪分析和情绪分类在分析客户审查、竞选候选人在选举中的受欢迎程度和对各种体育赛事的评论等应用中通常使用,但在本研究中,我们研究了他们用于流行病爆发检测的应用程序,但早期爆发检测是有效处理流行病的关键,然而,传统的爆发检测方法耗费时间,妨碍了各部门的迅速反应。Twitter、Facebook、Instagram等社交媒体平台使用户能够表达他们与生活不同方面有关的想法,从而成为这类情况下的重要信息来源。拟议研究利用了来自Twitter和新信网站的与巴基斯坦登革热流行有关的双语(Urdu和英语)数据,并进行了情绪分析和情绪分类,以便从数据集中获取与流行病爆发有关的公平想法的深刻见解。已经使用机器学习和深层次学习算法等社会媒体平台来培训和执行这两项任务的模式。对每种模型的比较性业绩进行了评估,使用了精确度、精确度、回顾和回顾。