The Emotion Analysis of Indian Political Tweets using Machine Learning

Parth Sharma, Mansi Vegad
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Abstract

In this day and age web-based entertainment is a major region for information examination and exploration work. For Feeling Examination, I select Tweeter handle. I use Tweepy for getting to tweeter information. I perform opinion examination on Indian Political information. I got 117545 tweets of 2019 Indian Political race. I use SVM (Backing Vector Machine) Classifier for feeling Examination. Feeling assessment oversees recognizing and portraying evaluations or sentiments conveyed in source message. Electronic diversion is creating an enormous proportion of feeling rich data as tweets, sees, blog sections, etc. Feeling examination of this client made data is especially useful in knowing the appraisal of the gathering. Twitter feeling assessment is problematic stood out from general assessment examination on account of the presence of work related conversation words and erroneous spellings. The most outrageous limitation of characters that are allowed in Twitter is 140. Data base philosophy and AI approach are the two frameworks used for separating suppositions from the text. In this paper, we endeavor to analyze the twitter posts about electronic things like mobiles, workstations, etc using AI approach.
利用机器学习对印度政治推文进行情感分析
当今时代,网络娱乐是信息检查和探索工作的主要区域。为了进行感受检查,我选择了 Tweeter 手柄。我使用 Tweepy 获取推特信息。我对印度政治信息进行舆论检查。我得到了 2019 年印度政治竞选的 117545 条推文。我使用 SVM(支持向量机)分类器进行感觉检查。感觉评估负责识别和描绘源信息中传达的评价或情感。电子游戏正在产生大量富含情感的数据,如推文、视频、博客等。对这些客户制作的数据进行感觉检查,尤其有助于了解对集会的评价。由于存在与工作相关的对话词汇和错误拼写,推特感受评估从一般评估检查中脱颖而出,问题重重。Twitter允许使用的最多字符为140个。数据库哲学和人工智能方法是用于从文本中分离假设的两个框架。在本文中,我们致力于使用人工智能方法分析有关手机、工作站等电子产品的 Twitter 帖子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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