Analyzing Social Media Sentiment: Twitter as a Case Study

IF 1.7 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-06-05 DOI:10.14201/adcaij.28394
Y. Jasim, M. G. Saeed, M. Raewf
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Abstract

This study examines the problem of Twitter sentimental analysis, which categorizes Tweets as positive or negative. Many applications require analyzing public mood, including organizations attempting to determine the market response to their products, political election forecasting, and macroeconomic phenomena such as stock exchange forecasting. Twitter is a social networking microblogging and digital platform that allows users to update their status in a maximum of 140 characters. It is a rapidly expanding platform with over 200 million registered users, 100 million active users, and half of the people log on every day, tweeting out over 250 million tweets. Public opinion analysis is critical for applications, including firms looking to understand market responses to their products, predict political choices, and forecast socio-economic phenomena like bonds. Through the deep learning methodologies, a recurrent neural network with convolutional neural network models was constructed to do Twitter sentiment analysis to predict if a tweet is positive or negative using a dataset of tweets. The applied methods were trained using a publicly available dataset of 1,600,000 tweets. Several model architectures were trained, with the best one achieving a (93.91%) success rate in recognizing the tweets' matching sentiment. The model's high success rate makes it a valuable advisor and a technique that might be improved to enable an integrated sentiment analyzer system that can work in real-world situations for political marketing.
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分析社交媒体情绪:以Twitter为例
本研究考察了推特情感分析的问题,将推特分类为积极或消极。许多应用程序需要分析公众情绪,包括试图确定市场对其产品的反应的组织、政治选举预测和宏观经济现象(如股票交易预测)。推特是一个社交网络微博和数字平台,允许用户更新最多140个字符的状态。它是一个快速发展的平台,拥有超过2亿注册用户,1亿活跃用户,每天有一半的人登录,发布超过2.5亿条推文。舆论分析对应用程序至关重要,包括公司希望了解市场对其产品的反应,预测政治选择,以及预测债券等社会经济现象。通过深度学习方法,构建了一个带有卷积神经网络模型的递归神经网络,使用推文数据集进行推特情绪分析,预测推文是积极的还是消极的。应用的方法是使用160万条推文的公开数据集进行训练的。训练了几种模型架构,其中最好的模型在识别推文的匹配情绪方面达到了93.91%的成功率。该模型的高成功率使其成为一种有价值的顾问和一种可以改进的技术,使集成的情绪分析系统能够在现实世界的情况下用于政治营销。
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
22
审稿时长
4 weeks
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