Sentiment Analysis of Imran Khan’s Tweets

Sadia Saeed, Tehseen Zahra, Asim Ali Fayyaz
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Abstract

In the recent past, sentiment analysis has been an area of interests of psychologists, sociologists, neurologists, computer scientists, and linguists including corpus linguists and computational linguists. Interdisciplinary approaches to researching various issues especially the analysis of social media websites such as Facebook, Twitter, and Instagram are becoming popular nowadays. The availability of data on social media has made it easier to analyse the opinion or sentiments of its users. Analysis of these sentiments could reveal the face of users and it could help in various decision-making processes. Sentiment analysis is a system of knowing polarity (positive, negative, and neutral) in discourse. Moreover, sentiments can enable and disable certain functions of discourse and can divert the attention of the audience from important to a less important issue or otherwise, hence, there is a need to analyse the sentiments. In this research, sentiments (Polarity) of Imran Khan’s tweets are analysed with the help of R studio. Data for this study is collected from Imran Khan’s one-year’s tweets, tweeted from 1st January 2018 to 20th November 2018. Later we saved the data in. csv files. The results of the polarity check revealed that he has used all three types of sentiments that is positive, negative, and neutral. However, he mostly used neutral or free polarity items (FPIs) that is 67.41% in his tweets. Among positive and negative polarity items the number of negative polarity items (NPIs) is higher that is 23.21% as compared to positive polarity items (PPIs) which are only 9.40%. The manual analysis of results revealed that only software is not enough and there is a need to check the accuracy of the results manually. The use of negative polarity/negative face reveals that he tries to be independent and autonomous in his decisions (Goffman, 1967). The use of positive polarity items shows he tries to show his positive face to others. Moreover, sentiment analysis demonstrates the presence of themes propagated through the use of various lexical items.
伊姆兰·汗推文情感分析
最近,情感分析一直是心理学家、社会学家、神经学家、计算机科学家和语言学家(包括语料库语言学家和计算语言学家)感兴趣的领域。研究各种问题的跨学科方法,尤其是对Facebook、Twitter和Instagram等社交媒体网站的分析,如今正变得越来越流行。社交媒体上数据的可用性使得分析用户的观点或情绪变得更加容易。对这些情绪的分析可以揭示用户的面貌,并有助于各种决策过程。情感分析是一种认知话语极性(积极、消极、中性)的系统。此外,情感可以启用或禁用话语的某些功能,并可以将听众的注意力从重要的问题转移到不太重要的问题或其他问题上,因此,有必要分析情感。在本研究中,在R studio的帮助下,分析了Imran Khan的推文的情绪(极性)。这项研究的数据收集自伊姆兰·汗一年的推文,从2018年1月1日到2018年11月20日。后来我们把数据保存在。csv文件。极性检查的结果显示,他使用了所有三种类型的情绪,即积极,消极和中性。然而,他的推文大多使用中性或自由极性(fpi),占67.41%。在正极性和负极性项目中,负极性项目(npi)的数量为23.21%,而正极性项目(ppi)的数量仅为9.40%。手工分析结果表明,只有软件是不够的,需要手工检查结果的准确性。消极极性/消极面孔的使用表明他在做决定时试图独立自主(Goffman, 1967)。积极极性项目的使用表明他试图向别人展示他积极的一面。此外,情感分析表明,通过使用各种词汇项传播主题的存在。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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