使用Flask环境对TwitterFeeds的情绪分析:数据分析的优越应用
Astha Modi1, Khelan Shah1, Shrey Shah1
1Department of Information and Communication Technology, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat 382426 India.
概括
本研究介绍了使用Flask环境进行推特情绪分析的通用方法. 它分析了各种公司的公众意见,将推特分类为积极,消极或中立.
科学领域:
- 计算机科学 计算机科学
- 自然语言处理自然语言处理.
- 数据科学数据科学数据科学
背景情况:
- 像Twitter这样的社交媒体平台对于共享数据和表达公众意见至关重要.
- 分析Twitter上大量的数据是了解公众情绪的首要任务.
- 情感分析是对主观文本数据中的情绪分类的关键技术.
研究的目的:
- 开发一种使用Flask环境进行推特情绪分析的通用方法.
- 分析和比较不同行业对跨国公司的情绪.
- 为了可视化情感分析结果,包括语言分析和推特细节.
主要方法:
- 使用Flask环境进行文本情绪分析,将文本情绪分析分为积极,消极和中立的类别.
- 实现对Twitter开发者帐户进行API调用以获取数据.
- 开发一个网页以显示情绪分析结果,包括派图表和推特详细信息.
主要成果:
- 该系统成功地获取并分析了推特数据以进行情绪分类.
- 情绪分析结果在网页上可视化,显示正面,负面和中性推文的百分比.
- 对企业,运动服装和多媒体行业的公司进行了比较情绪分析.
结论:
- 弗拉斯克环境为通用推特情绪分析提供了一个强大的框架.
- 开发的系统有效地可视化了公众情绪,并促进了跨行业的比较分析.
- 这种方法提供了基于社交媒体数据对大公司的公众看法有价值的见解.
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