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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-and-lexicon-approach-to-sentiment-analysis/
课程评论：没有评论
课程名称：机器学习与情感分析的词典方法 课程概述：本课程深入探讨如何利用Twitter数据，帮助学员连接并下载推文，通过Twitter API获取社交媒体趋势或进行情感分析。您将从学习如何访问Twitter的庞大数据集开始，下载与您所选择主题相关的推文。 在收集数据后，课程将指导您清理和预处理数据，将原始推文转化为适合分析的结构化数据。随后，您将进入情感分析的精彩领域，学习两种最常用的方法。第一种是基于词典的方法，您将利用预构建的词典快速有效地判断文本的情感。这种方法非常适合没有深厚机器学习知识的初学者。 第二种方法更为先进，利用机器学习训练自定义模型，基于标记数据进行模型训练。在模型训练完成后，您将能够将其应用于新数据，实现情感预测的精准度不断提升。 课程结束时，您将构建一个强大的脚本，能够分析数百或数千条推文的情感，适用于您选择的任何主题。无论您是初学者还是希望扩大数据科学工具箱的学员，本课程将为您提供在真实世界Twitter数据上进行情感分析的实用技能和知识。
Unlock the power of Twitter data with this in-depth course on connecting to and downloading tweets through the Twitter API. Whether you're interested in analyzing social media trends or conducting sentiment analysis for research or business, this course will guide you every step of the way. You'll start by learning how to access Twitter's vast data through its API, downloading tweets that are relevant to your chosen topic.Once you've gathered your data, I'll show you how to clean and preprocess it, transforming raw tweets into structured data that's ready for analysis. From there, we'll dive into the fascinating world of sentiment analysis, exploring the two most commonly used approaches. The first is the Lexicon-based approach, where you'll leverage pre-built lexicons to determine the sentiment of given text quickly and effectively. This method is great for those looking to get started with sentiment analysis without deep machine learning knowledge.The second approach is more advanced, using Machine Learning to train a custom model on labeled data. Once your model is trained, you'll apply it to new data, allowing it to predict sentiment with increasing accuracy. By the end of the course, you'll have built a powerful script capable of analyzing the sentiment of hundreds or even thousands of tweets on any topic you choose. Whether you're a beginner or looking to expand your data science toolkit, this course will equip you with practical skills and knowledge to perform sentiment analysis on real-world Twitter data.
