Author: Zen and the Art of Computer Programming
1 Introduction
Natural language processing (NLP) is a field of computer science that studies how to process and use natural language. One of the most important tasks is sentiment analysis, which is to extract the true meaning of emotions, attitudes, or preferences from text data. In this article, I will show you an approach based on the Python library TextBlob, a tool for simple and fast sentiment analysis. You can think of it as a black box toolbox that can be applied to any task that requires sentiment analysis. In order to make it easier for readers to understand and use, the content of this article is mainly intended for data scientists and machine learning engineers. However, students who are interested in this are also welcome to read and participate in discussions.
2. Relevant background introduction
Before getting into the topic, it is necessary to make some brief introduction to the following concepts and nouns.
2.1 What is sentiment analysis?
Sentiment analysis refers to the process of automatically identifying, classifying and evaluating positive or negative emotions, opinions, attitudes and other characteristics in electronic or media documents and texts. It is usually used to detect customers' attitudes towards something. And then influence business decisions.
2.2 Why perform sentiment analysis?
Sentiment and behavior analysis of user feedback: Through sentiment analysis, companies can understand users' satisfaction, love, recognition, satisfaction, etc. with products or services, thereby improving product quality and shaping consumer psychology.
Brand marketing and marketing: Using social network dynamics on the Internet and social media, users' attitudes and preferences for a certain product or service can be analyzed, and corresponding products or services can be recommended through keywords to increase product visibility.
Intelligence analysis and public opinion monitoring: Using big data and artificial intelligence technology, the massive comments, discussions, reports and other information generated by users can be analyzed to discover market hot spots, public opinion events, policy risks, etc.