How to use MySQL database for text analysis?

How to use MySQL database for text analysis?

With the advent of the big data era, text analysis has become a very important technology. As a popular relational database, MySQL can also be used for text analysis. This article will introduce how to use MySQL database for text analysis and provide corresponding code examples.

1. Create database and table

First, we need to create a MySQL database and table to store text data. You can use the following SQL statement to create a database named "analysis" and a table named "text_data".

CREATE DATABASE analysis;
USE analysis;
CREATE TABLE text_data (
    id INT PRIMARY KEY AUTO_INCREMENT,
    content TEXT
);

2. Import text data

The next step is to import the text data to be analyzed into the MySQL database. This can be achieved using the LOAD DATA INFILE statement or the INSERT INTO statement.

If the text data is saved in a CSV file, you can use the following SQL statement to import the data:

LOAD DATA INFILE 'path/to/text_data.csv'
INTO TABLE text_data
FIELDS TERMINATED BY ','
ENCLOSED BY '"'
LINES TERMINATED BY '
'
IGNORE 1 ROWS;

If the text data is saved in a other type of file, you can use the corresponding method to import the data. It reads into memory and then inserts the data into the table using the INSERT INTO statement.

3. Text Analysis

Once the data is imported into the MySQL database, you can use SQL statements for text analysis. The following are some commonly used text analysis operations and corresponding SQL statement examples:

• Count number of texts:

SELECT COUNT(*) FROM text_data;

• Count number of words:

SELECT SUM(LENGTH(content) - LENGTH(REPLACE(content, ' ', '')) + 1) FROM text_data;

• Find text that contains specific keywords:

SELECT * FROM text_data WHERE content LIKE '%keyword%';

• Find the most frequently occurring words:

SELECT word, COUNT(*) AS count FROM (
    SELECT DISTINCT SUBSTRING_INDEX(SUBSTRING_INDEX(content, ' ', n), ' ', -1) AS word
    FROM text_data
    JOIN (
        SELECT 1 AS n UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT 4
    ) AS numbers
    ON CHAR_LENGTH(content) - CHAR_LENGTH(REPLACE(content, ' ', '')) >= n - 1
) AS words
GROUP BY word
ORDER BY count DESC
LIMIT 10;

• Find The most common two-word combination:

SELECT CONCAT(word1, ' ', word2) AS phrase, COUNT(*) AS count FROM (
    SELECT DISTINCT
        SUBSTRING_INDEX(SUBSTRING_INDEX(content, ' ', n1), ' ', -1) AS word1,
        SUBSTRING_INDEX(SUBSTRING_INDEX(content, ' ', n2), ' ', -1) AS word2
    FROM text_data
    JOIN (
        SELECT a.n + b.n * 10 AS n1, a.n + b.n * 10 + 1 AS n2
        FROM (
            SELECT 1 AS n
            UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5
            UNION ALL SELECT 6 UNION ALL SELECT 7 UNION ALL SELECT 8 UNION ALL SELECT 9
        ) AS a
        CROSS JOIN (
            SELECT 0 AS n UNION ALL SELECT 1 UNION ALL SELECT 2 UNION ALL SELECT 3
        ) AS b
    ) AS numbers
    ON CHAR_LENGTH(content) - CHAR_LENGTH(REPLACE(content, ' ', '')) >= n2 - 1
) AS phrases
GROUP BY phrase
ORDER BY count DESC
LIMIT 10;

4. Result display and visualization

Finally, we can use MySQL’s result set and other visualization tools (such as Python Matplotlib, Tableau, etc.) to display the analysis results.

For example, you can use the following Python code to use Matplotlib to generate a histogram showing the frequency of each word:

import matplotlib.pyplot as plt
import mysql.connector

cnx = mysql.connector.connect(user='your_username', password='your_password',
                              host='localhost',
                              database='analysis')
cursor = cnx.cursor()

query = ("SELECT word, COUNT(*) AS count FROM ("
         "SELECT DISTINCT SUBSTRING_INDEX(SUBSTRING_INDEX(content, ' ', n), ' ', -1) AS word "
         "FROM text_data "
         "JOIN ("
         "SELECT 1 AS n UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT 4"
         ") AS numbers "
         "ON CHAR_LENGTH(content) - CHAR_LENGTH(REPLACE(content, ' ', '')) >= n - 1"
         ") AS words "
         "GROUP BY word "
         "ORDER BY count DESC "
         "LIMIT 10")

cursor.execute(query)

words = []
counts = []

for (word, count) in cursor:
    words.append(word)
    counts.append(count)

plt.bar(words, counts)
plt.xlabel('Word')
plt.ylabel('Count')
plt.title('Frequency of Top 10 Words')
plt.xticks(rotation=45)
plt.show()

cursor.close()
cnx.close()

The above are the basic steps and sample code for text analysis using the MySQL database. I hope it can help you in your text analysis work in actual projects.

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