Twitter Sentiment Analysis Using Lexical or Rule Based Approach: A Case Study
Twitter Sentiment Analysis Using Lexical or Rule Based Approach: A Case Study
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使用基于词汇或规则的方法进行 Twitter 情绪分析：案例研究
DOI：
10.1109/icrito48877.2020.9197910
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Rajesh Rohilla
中科院分区：
文献类型：
--
作者：
Sheresh Zahoor;Rajesh Rohilla
观点分析或情绪分析是当今最有效的技术之一，用来确定人们对任何事件的情绪或情绪。这种技术之所以出现，是因为人们广泛使用Facebook、Twitter等社交媒体平台来表达他们对任何已经发生的事件或任何最有可能发生的事件的情绪，无论是电影的上映还是即将举行的政治集会。人们一定要表达他们的情感。事实证明，情绪分析对任何销售产品的公司都非常有用，这样就可以知道人们或任何政党对他们的产品的接受程度，以确定人们对他们的候选人的反应。为了分析这些情绪，可以使用两种机器学习方法--无监督或有监督。在本文中，我们使用了非监督方法，这是一种基于规则或词法的方法，可以使用预先构建的开源库，如TextBlob，Vader来完成。
Opinion analysis or sentiment analysis is one of the most effective techniques these days in order to determine the sentiments or emotions of people regarding any event. This technique has come to the fore because of extensive use of social media platforms like Facebook, Twitter etc. by people to express their emotions regarding any event that has occurred or any event that is most likely to happen, be that the release of a movie or a political rally that is about to take place. People make sure to express their sentiments. Sentiment analysis proves very beneficial for any company selling a product to know how their product was received by people or by any political party to determine how people are reacting towards their running candidate. In order to analyze these sentiments two approaches of machine learning can be used – unsupervised or supervised. In this paper we have worked with Unsupervised Approach which is a rule based or lexical approach and can be done using the pre-built open source libraries like TextBlob, VADER.