A Comparative Study of Sentiment Analysis Tools

Nabanita Das, Saloni Gupta, Srinjoy Das, Shuvam Yadav, Trishika Subramanian, Nairita Sarkar
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Abstract

COVID-19 outbreak compelled people to stay at home due to complete lockdown in all the working areas. Immense use of World Wide Web and social media to exchange and share opinions, generated enormous web data to be utilized in the research work of the Natural Language Processing (NLP) field. Being a dominant side of NLP, Sentiment Analysis uses numerous tools to classify human sentiments as Positive (1), Negative (-1) and Neutral (0) so as to reach various conclusions. This research work focused on sentiment analysis of four datasets, web scraped from four different sources namely: Twitter, Facebook, Economic Times Headlines and news articles keyed by stock market. Seven contemporary and tremendously used sentiment analysis tools: Stanford, SVC, TextBlob, Henry, Loughran-McDonald, Logistic Regression and VADER are considered here to process four scraped datasets individually and analyses result in two ways: Facebook scraped data generates maximum overall positive sentiment score as 38.17% and VADER tool performs best among seven tools. VADER calculates overall positive sentiment score as 56.63%
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情感分析工具的比较研究
由于新冠肺炎疫情的爆发,所有工作区域都被完全封锁,人们不得不呆在家里。大量使用万维网和社交媒体来交换和分享意见,产生了大量的网络数据,用于自然语言处理(NLP)领域的研究工作。情绪分析是NLP的一个主要方面,它使用许多工具将人类的情绪分为Positive (1), Negative(-1)和Neutral(0),从而得出各种结论。这项研究工作侧重于对四个数据集的情绪分析,这些数据集来自四个不同的来源,即:Twitter、Facebook、《经济时报》头条新闻和以股市为关键的新闻文章。七个当代和广泛使用的情绪分析工具:斯坦福,SVC, TextBlob, Henry, loughranmcdonald, Logistic Regression和VADER在这里被认为单独处理四个抓取数据集,并以两种方式分析结果:Facebook抓取数据产生最大的整体积极情绪得分为38.17%,VADER工具在七个工具中表现最好。维德计算出总体积极情绪得分为56.63%
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