使用基于置信区间的在线产品品牌分类的高效社交媒体情感分析

IF 0.4 Q4 MULTIDISCIPLINARY SCIENCES International Journal of Advanced and Applied Sciences Pub Date : 2023-10-01 DOI:10.21833/ijaas.2023.10.011
Tawfik Guesmi, Fawaz Al-Janfawi, Ramzi Guesmi, Mansoor Alturki
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引用次数: 0

摘要

本文提出了一种有效的方法来对互联网用户的情绪进行分类,重点是社交媒体用户,使用置信区间来估计情绪预测的可靠性。分类是根据他们在帖子中表达的情绪,分为积极、消极和中性三类。本文提出了一个分析表,用来分析人们对网络产品品牌的看法和看法。该过程包括两个步骤:1)使用机器学习技术从文本数据中分析情感;2)描述一个五步情感和观点分类过程,包括数据收集、预处理、算法应用、验证和可视化。提出的解决方案是使用Python以及scikit-learn、NumPy、pandas和Dash库实现的,并利用置信区间来评估情感分析模型的准确性和可靠性。
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Efficient social media sentiment analysis using confidence interval-based classification of online product brands
This paper presents an efficient method for categorizing the sentiments of Internet users, with a focus on social media users, using a confidence interval to estimate the reliability of sentiment predictions. The classification is based on the sentiments expressed in their posts, which are divided into positive, negative, and neutral categories. The paper presents an analysis table that analyzes sentiments and opinions about online product brands. The process includes two steps: 1) analyzing sentiments from text data using machine learning techniques, and 2) describing a five-step sentiment and opinion classification process that includes data collection, preprocessing, algorithm application, validation, and visualization. The proposed solution is implemented using Python, along with the scikit-learn, NumPy, pandas, and Dash libraries, and leverages the use of confidence intervals to assess the accuracy and reliability of the sentiment analysis model.
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来源期刊
CiteScore
0.80
自引率
0.00%
发文量
234
审稿时长
8 weeks
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