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Towards Sentiment Analysis and Product Identification of Tobacco-Related Text in Social MediaAuthor: Venkata Himakar Yanamandra Date: 2022-02-18 Report no: IIIT/TH/2022/39 Advisor:Radhika Mamidi AbstractDuring Covid-19, we have seen experts from various fields come together and collaborate for collective social good. NLP and AI have been employed to create a positive impact in recent years, especially in public health research. In the work-from-home world, 500 thousand tweets are posted every day and 4 petabytes of data is created on Facebook1 . This plethora of data can be utilized for online biosurveillance to early insights into potential future epidemics. Today, smoking tobacco is one of the leading causes of preventable death. It causes more than 8 million deaths per year worldwide 2 . Real-time monitoring of public sentiment and trends helps us understand the gravity of potential health threats like smoking tobacco and help create necessary cessation and preventive campaigns. Contrary to previous tobacco studies, we have taken the language of the demographic affected, street words, and colloquial slang related to smoking into account. We released the smokeng dataset, a general tobacco-related dataset consisting of 3144 tweets along with a comprehensive annotation schema. Each class is created and annotated based on the content of the tweets such that further hierarchical methods can be easily applied. While cigarette smoking went down among high school students from 2011 to 2019, the number of students using e-cigarettes rose from 3.6 million to 5.4 million 3 . As the e-cigarette problem has exponentially increased, the need to treat smoking products individually presented itself. The target audience varies and might use different language and sentiments to describe the product they use. We extended Smokeng on tobacco product and product sentiment axes to release the SmokPro and SentiSmoke-Twitter datasets along with a comprehensive annotation schema for identifying tobacco products and their respective sentiments. Contemporary tobacco-related studies are primarily concerned with a single social media platform while missing a broader audience. Moreover, they are heavily reliant on labeled datasets, which are expensive to make. We explore sentiment and product identification on tobacco-related text from two social media platforms. Extending SentiSmoke-Twitter dataset, we created SentiSmoke-Reddit product and sentiment datasets by the application of transfer learning. To the best of our knowledge, this was first cross-OSMplatform attempt in topical tobacco research using semi-supervised learning. Further, we prove the efficacy of standard text classification methods on the above datasets by designing Full thesis: pdf Centre for Language Technologies Research Centre |
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