Class introduction

▪ The Python text mining class deals with text data analysis, including Hangul.

This class refines text and documents, which are unstructured data, from basic analysis to sentiment analysis, and clustering of similar semantic groups

It deals with advanced analysis and has a single actual paper topic, collection methods, refinement, basic analysis, association analysis, sentiment analysis,

It covers cluster and topic modeling, and the latest techniques such as word2vec and doc2vec in detail

It is a lecture specialized in Hangul text mining, and the core concepts of text analysis, practical topics for each course, and collisions in the analysis process

You can master practical text analysis, such as problems and solutions.

Class goals

- You will understand the structure of text data, which is unstructured data, and understand the analysis and overall context.

- You will learn the importance of the pre-processing process of text data, and learn pre-processing methods and key checkpoints.

- You will understand basic analysis, association analysis, sentiment analysis, cluster, and topic modeling as analytical methods of text analysis.

- You can perform all analyses, including the latest techniques such as word2vec and doc2vec.

- By performing the following steps using an actual text data set, you can fully utilize it in practice.

* Collection of English and Hangul → Purification → Word Cloud → Association Analysis → Sentiment Analysis → Clustering & Topic Modeling → word2vec, doc2vec