基于特征的社交媒体情感分析机器学习预测模型

Jessica Sarah Deen, Juan Mark Deen, Amisha Michelle Danny, Arien Maxwell Danny, Marc Ruben Danny
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引用次数: 0

摘要

情感分析是发现一群人当前的思想观点及其想法。情感分析以人们在社交媒体平台上的自然反应为基础,反映他们的状态和状态。情感分析的主要目的是处理社会环境及其对媒体世界和周围环境的影响。然而,这是了解世界各个角落的关键任务。情感进化模拟情感行为,以显示不同时间的不同反应和情感方向。它可以帮助用户更深入、更详细地了解用户提供的内容所代表的观点和态度。期刊、论坛、博客、微博、推特和社交网络等社交媒体平台的发展促进了情感分析的发展。企业的竞争优势在于收集企业社交媒体并实施机器学习算法,从而获得有价值的见解。在本研究中,我们的任务是展示基于词袋(BoW)和术语-频率-反向文档-频率(tf_idf)特征的机器学习预测模型,这些模型可以帮助进行情感分析,并找出客户对公司产品的需求和期望。市场研究可能是情感分析应用中最重要的领域,除了品牌认知、客户意见调查和反馈之外。本研究的结果分析表明了将社交媒体推文反馈分为积极或消极类别的关键方法,通过使用分类器作为基线,证明了基于特征的评论对于任何商业模型及其结果的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Features Based Machine Learning Prediction Model for Sentiment Analysis on Social Media
Sentiment analysis is discovering the current ideology opinion of a group of people and their thoughts. The Sentiment analysis based onthe natural reaction of people on social media platform to reflect their mantel status and state. The main poupose of sentiment analysis is to dealing with society's environment and its impact effects towards the media world and surrounding environment. However, this is the key task of understanding every part of the world. The evolution of feeling simulates the sentiment behaviours to shows different direction of reactions and feeling across time. It can help users obtain a more advanced and detailed understanding of the views and attitudes represented in the content provided by users. The development of social media platforms, such as journals, forums, blogs, micro-blogs, Twitter, and social networks, has fostered sentiment analysis. Competitive advantages for organizations are collecting corporate social media and implementing machine learning algorithms to get valuable insights. In this study, our tasks are to show Bag of Words (BoW) and Term-Frequency-Inverse-Document-Frequency (tf_idf) feature-based machine learning prediction models that can help with sentiment analysis and figure out what their customers need and want from company items. Market research is perhaps the most important field for sentiment analysis applications, aside from brand perception and customer opinion surveys and feedbacks. This study results analysis shows the crucial way of classifying social media tweets feedback into positive or negative categories via using the classifier as a baseline to demonstrate in what manner comments are important based on features for any business model and their result.
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