使用深度学习技术的Twitter情感分析

S. Kasifa Farnaaz and A. Sureshbabu
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摘要

万维网认真地研究了个人表达他们对各种主题、模型和关注点的观点和结论的新方法。客户为各种媒体提供材料,如网络聚会、讨论组和网络日志,并为在推广和研究等领域获得影响力提供坚实而开放的基础。战略、论证研究、市场评估和商业视角都是重要的考虑因素。理论研究从公开可用的数据中排除推导,并将作者与给定对象相关联的情感组织为两种特定类别(积极和消极)之一。把这两个问题区别开来。这是在twitter投机审计周期之后快速寻找非结构化新闻。此外,我们正在寻找几种方法在Twitter新闻上呈现逐项的积极评价。它还显示了受感知边界影响的操作之间的参数关系。它们所传达的品质与推文有关:积极、消极或公平。这项工作将大致呈现在Twitter上的防御欣赏探索;它们所传达的品质与推文有关:积极、消极或公平。Twitter是一个基于网络的应用程序,集成了博客和广泛的联系人,允许用户发送140个字符的简短消息。这是一个快速发展的伙伴关系,有超过2亿的赞助者,其中1亿是活跃的客户,其中很大一部分人定期关注Twitter,发送了超过2.5亿条推文。本研究旨在使用双字母和三字母的深度学习进行情感分析,以准确分类推文。
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Twitter Sentiment Analysis Using Deep Learning Techniques
The World Wide Web has taken a serious look at new ways for individuals to express their viewpoints and conclusions on a variety of topics, models, and concerns. Clients provide material for a variety of media, such as web gatherings, discussion groups, and weblogs, and provide a robust and open foundation for gaining clout in areas such as promoting and research. Strategy, justification research, market estimations, and a business perspective are all important considerations. Theory study eliminates derivations from publicly available data and organizes the sentiments that the author associates with a given object into one of two specified categories (positive and negative). Make a distinction between the two problems. This follows a Twitter speculation audit cycle for quickly seeking unstructured news. Furthermore, we're looking at several ways to present an itemized positive assessment on Twitter News. It also shows a parametric relationship between operations that are influenced by perceived boundaries. The qualities conveyed in them address the tweets: positive, negative, or fair. This work will in general present the defense appreciate exploring on Twitter; the qualities conveyed in them address the tweets: positive, negative, or fair. Twitter is a web-based application that integrates with a blog and a wide range of contacts, allowing users to send brief 140-character messages. It's a rapidly growing partnership with over 200 million endorsers, 100 million of whom are active clients, and a large portion of them follow Twitter on a regular basis, sending out over 250 million tweets. This study aims to perform Sentimental analysis using deep learning with bigrams and trigrams to classify the tweets accurately.
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