审美整形手术的情感理解

A. Choudhary, E. Cambria
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引用次数: 0

摘要

随着社交媒体渗透到我们生活的方方面面,网民表达的意见是一座金矿,随时可以被利用,以一种有意义的方式影响所有主要的公共事务。情感分析是一种使用人工智能工具解释这种非结构化数据的方法。受新冠肺炎疫情影响,美容整形领域出现了“变焦热潮”,这是众所周知的事实,人们的注意力也集中在了我们的外表上。对新冠肺炎疫情前后发布的流行整形手术推文进行极性检测,可以为整形外科医生和整个健康行业提供很好的见解。在这项工作中,我们开发了一个端到端系统,用于对此类推文进行情感分析,该系统结合了最先进的微调深度学习模型、巧妙的“关键字搜索和过滤方法”以及SenticNet。我们的系统在一个包含196,900条推文的大型数据库上进行了测试,使用有效校正的词云将结果可视化,并进行了严格的统计假设检验,以得出有意义的推论。结果具有高度的统计学意义。
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Making Sense of Sentiments for Aesthetic Plastic Surgery
With social media pervading all aspects of our life, the opinions expressed by netizens are a gold mine ready to be exploited in a meaningful way to influence all major public do-mains. Sentiment analysis is a way to interpret this unstructured data using AI tools. It is a well-known fact that there has been a 'Zoom Boom’ in the field of aesthetic plastic surgery due to the COVID-19 pandemic and the same has put the focus of attention sharply on our appearance. Polarity detection of tweets published on popular aesthetic plastic surgery procedures before and after the onset of COVID can provide great insights for aesthetic plastic surgeons and the health industry at large. In this work, we develop an end-to-end system for the sentiment analysis of such tweets incorporating a state-of-the-art fine-tuned deep learning model, an ingenious 'keyword search and filter approach’ and SenticNet. Our system was tested on a large database of 196,900 tweets and the results were visualized using affectively correct word clouds and also subjected to rigorous statistical hypothesis testing to draw meaningful inferences. The results showed a high level of statistical significance.
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