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Digital Marketing with Social Media: What Twitter Says!

机译:社交媒体的数字营销:推特怎么说!

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Due to the short and simple way of expression on social media platforms such as Facebook and Twitter, millions of people share daily real-time thoughts and opinions about everything. This shared data generates an increasing availability of unstructured, informal and yet valuable information to data science researchers. Traditional approaches are not the wisest path for collecting and studying consumer behavior because they require a large amount of time and resources and therefore lead to considerable losses for companies. In this paper, we develop a system able to identify and classify sentiment represented in an electronic text from Twitter where users post real-time reactions and opinions called tweets; that are sentences limited to 280 characters about everything to improve the decision-making process for companies. To do so, we used tweepy to access Twitters Streaming API, we combined natural language processing techniques with naive Bayes networks to classify users data, we used GIS (geographical information system) and Matplotlib for data visualization and displaying the results. The purpose of this paper is to propose an efficient approach for predicting accurate sentiment from raw unstructured data in order to extract opinions from the Internet and predict online customers preferences, which could be valuable and crucial for economic and marketing researchers.
机译:由于在诸如Facebook和Twitter之类的社交媒体平台上进行简短表达的方式,数以百万计的人每天都在实时共享有关所有内容的想法和观点。这种共享的数据为数据科学研究人员提供了越来越多的非结构化,非正式但有价值的信息。传统方法不是收集和研究消费者行为的最明智的方法,因为它们需要大量的时间和资源,因此会给公司造成可观的损失。在本文中,我们开发了一个系统,该系统能够识别和分类来自Twitter电子文本的情感,用户可以在其中发布实时反应和意见,称为推文;句子的长度不得超过280个字符,以改善公司的决策过程。为此,我们使用tweepy访问Twitters Streaming API,将自然语言处理技术与朴素的贝叶斯网络相结合,对用户数据进行分类,并使用GIS(地理信息系统)和Matplotlib进行数据可视化并显示结果。本文的目的是提出一种有效的方法,用于从原始的非结构化数据中预测准确的情绪,以便从Internet上提取意见并预测在线客户的偏好,这对于经济和市场研究人员而言可能是有价值的且至关重要的。

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