textblob 情感分析_如何使用TextBlob在Python中构建Twitter情感分析器

textblob 情感分析

by Arun Mathew Kurian

通过阿伦·马修·库里安(Arun Mathew Kurian)

如何使用TextBlob在Python中构建Twitter情感分析器 (How to build a Twitter sentiment analyzer in Python using TextBlob)

This blog is based on the video Twitter Sentiment Analysis — Learn Python for Data Science #2 by Siraj Raval. In this challenge, we will be building a sentiment analyzer that checks whether tweets about a subject are negative or positive. We will be making use of the Python library textblob for this.

该博客基于Twitter情绪分析视频-Siraj Raval的《 学习Python for Data Science＃2》 。 在这一挑战中，我们将构建一个情绪分析器，以检查有关某个主题的推文是否为正面或负面。 我们将为此使用Python库textblob。

Sentiment Analysis, also called opinion mining or emotion AI, is the process of determining whether a piece of writing is positive, negative, or neutral. A common use case for this technology is to discover how people feel about a particular topic. Sentiment analysis is widely applied to reviews and social media for a variety of applications.

情感分析，也称为观点挖掘或情感AI，是确定某篇文章是正面的，负面的还是中立的过程。 该技术的一个常见用例是发现人们对特定主题的感觉。 情绪分析已广泛应用于评论和社交媒体，具有多种应用。

Sentiment analysis can be performed in many different ways. Many brands and marketers use keyword-based tools that classify data (i.e. social, news, review, blog, etc.) as positive/negative/neutral.

情感分析可以通过许多不同的方式进行。 许多品牌和营销商使用基于关键字的工具将数据(即社交，新闻，评论，博客等)分类为正面/负面/中性。

Automated sentiment tagging is usually achieved through word lists. For example, mentions of ‘hate’ would be tagged negatively.

自动情感标记通常是通过单词列表来实现的。 例如，对“仇恨”的提及会被加上负面标签。

There can be two approaches to sentiment