根据 COVID 疫苗接种相关推文进行子话题层面的意见分类。

IF 1.1 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Mrinmoy Sadhukhan, Pramita Bhattacherjee, Tamal Mondal, Sudakshina Dasgupta, Indrajit Bhattacharya
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引用次数: 0

摘要

冠状病毒病 2019(Covid-19)是一种传染性疾病,在全球范围内影响了大量人口,死亡率很高。为应对近期 COVID-19 的传播,首要措施之一是为人们全面接种疫苗。全球各地的人们对疫苗接种过程、其副作用和效果有着不同的看法。这些意见会在包括 twitter 在内的不同微博网站上传播。通过分析此类微博的公众情绪来进行意见挖掘是检测公众反应的常用方法。本文主要从子话题层面对与 COVID-19 疫苗接种相关的公众意见进行分类。该程序试图找出有关正面、负面和中性句子的不同关键词。从这些关键词中,使用 Rocchio 查询扩展算法为正面、负面和中性情绪构建了不同的相关查询集。之后,扩展查询集使用 LDA 算法形成子主题,以识别推文的性质。所提出的 LDA 模型与 20 个子主题的一致性得分为 0.56,足以将推文分为不同的类别。这个训练有素的模型最终被用于使用 Apache Kafka 框架根据正面、负面或中性情绪对不同子主题的推文进行实时分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Opinion classification at subtopic level from COVID vaccination-related tweets.

Opinion classification at subtopic level from COVID vaccination-related tweets.

Opinion classification at subtopic level from COVID vaccination-related tweets.

Opinion classification at subtopic level from COVID vaccination-related tweets.

Coronavirus disease 2019 (Covid-19) is a contiguous disease which affected a large volume of population with a high mortality rate across the globe. For dealing with the recent spread of COVID-19, one of the prime measures was to vaccinate people in full extent. People across the globe have diverse opinion regarding the vaccination process, its side effect and effectiveness. Such opinions get located into different micro-blogging sites including twitter. Opinion mining through analyzing public sentiments of such micro-blogs is a common method for detection of public responses. This paper focuses on classifying the public opinions expressed related to COVID-19 vaccination at sub topic level. The procedure tries to find out different keywords regarding positive, negative and neutral sentences. From those keywords, different related query set was constructed using Rocchio query expansion algorithm for positive, negative and neutral sentiments. Later Extended query set is used to form subtopic using LDA algorithm to identify the nature of the tweets. The proposed LDA model came across with 0.56 coherence score with twenty subtopics, which is fair enough to classify the tweets in different classes. This trained model is finally used to classify the tweets in real time with Apache Kafka framework regarding different subtopic based on positive, negative or neutral sentiment.

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来源期刊
Innovations in Systems and Software Engineering
Innovations in Systems and Software Engineering COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
3.80
自引率
8.30%
发文量
75
期刊介绍: Innovations in Systems and Software Engineering: A NASA Journal addresses issues and innovations in Systems Engineering, Systems Integration, Software Engineering, Software Development and other related areas that are specifically of interest to NASA. The journal includes peer-reviewed world-class technical papers on topics of research, development and practice related to NASA''s missions and projects, topics of interest to NASA for future use, and topics describing problem areas for NASA together with potential solutions. Papers that do not address issues related to NASA are of course very welcome, provided that they address topics that NASA might like to consider for the future. Papers are solicited from NASA and government employees, contractors, NASA-supported academic and industrial partners, and non-NASA-supported academics and industrialists both in the USA and worldwide. The journal includes updates on NASA innovations, articles on NASA initiatives, papers looking at educational activities, and a State-of-the-Art section that gives an overview of specific topic areas in a comprehensive format written by an expert in the field.
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