使用基于词典的情感分析方法:印度社区对加密货币话题的看法分析

Q1 Decision Sciences Annals of Data Science Pub Date : 2023-10-25 DOI:10.1007/s40745-023-00496-y
Sankalp Loomba, Madhavi Dave, Harshal Arolkar, Sachin Sharma
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引用次数: 0

摘要

由于计算能力的不断提高和使用的便捷性,Facebook、Twitter 等社交网络平台已成为即时表达个人观点的流行媒介,无论是关于政治局势、商业产品还是社会事件。Twitter 是一个强大的信息源,其数据可以通过一个名为 "观点挖掘 "或 "情感分析 "的过程来调查用户的观点。本文利用自然语言处理和数据科学的原理,对多种基于词库的情感分析算法进行了比较评估,以从推文中提取民意。该研究利用数据科学方法探讨了情感分析的细微差别,评估了各种基于词典的算法如何成功识别和分类印度社区关于加密货币的推文中表达的情感。
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Sentiment Analysis using Dictionary-Based Lexicon Approach: Analysis on the Opinion of Indian Community for the Topic of Cryptocurrency

Due to the ever-increasing computing power and easy availability, social-networking platforms like Facebook, Twitter, etc. have become a popular medium to express one’s views instantly, be it about political situations, commercial products, or social occurrences. Twitter is a powerful source of information, whose data can be utilized to investigate the opinions of users through a process called Opinion Mining or Sentiment Analysis. Using the principles of Natural Language Processing and data science, this paper presents a comparative evaluation of multiple lexicon-based sentiment analysis algorithms to extract public opinion from tweets. The study explores the nuances of sentiment analysis using data science methodology, assessing how various lexicon-based algorithms may successfully identify and classify sentiments expressed in tweets from the Indian community about cryptocurrency.

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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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