Sentiment analysis is a powerful technique used to understand the emotions and opinions expressed in text data. It has a wide range of applications, such as customer feedback analysis, social media sentiment analysis, and brand perception monitoring.

In this blog post, we will explore how to perform sentiment analysis using SQL’s AVG function. While there are specialized libraries and tools available for sentiment analysis, using SQL can be a fast and efficient way to analyze text data directly in your database.

Prerequisites

To follow along with this tutorial, you should have a working knowledge of SQL and be familiar with relational databases. We’ll be using a SQLite database in our examples, but the concepts and techniques discussed can be applied to other database systems as well.

Sentiment Analysis with SQL

SQL’s AVG function is commonly used to calculate the average value of a numeric column. However, with a little bit of preprocessing, we can leverage this function to perform sentiment analysis on text data. Here’s how:

Step 1: Preprocess the Text Data

The first step in performing sentiment analysis with SQL is to preprocess the text data. This typically involves removing punctuation, converting all text to lowercase, and splitting the text into individual words or tokens.

For example, let’s say we have a table called reviews with a column text containing customer reviews. We can use SQL’s built-in string functions to preprocess the text data as follows:

SELECT
  REPLACE(REPLACE(REPLACE(REPLACE(LOWER(text), '.', ''), ',', ''), '?', ''), '!', '') AS processed_text
FROM
  reviews;

The above SQL query removes periods, commas, question marks, and exclamation marks, and converts the text to lowercase.

Step 2: Assign Sentiment Scores to Words

The next step is to assign sentiment scores to individual words or tokens in the preprocessed text. This step is crucial as it determines the sentiment intensity associated with each word.

For simplicity, let’s assume we have a table called sentiment_scores that contains a list of words and their corresponding sentiment scores. Each row in the table represents a word and its predefined sentiment score.

We can join the preprocessed text data with the sentiment_scores table to assign sentiment scores to words in the text:

SELECT
  r.processed_text,
  ss.sentiment_score
FROM
  reviews r
JOIN
  sentiment_scores ss ON r.processed_text LIKE '%' || ss.word || '%';

In the above query, we use the LIKE operator and concatenation to match words in the preprocessed text with words in the sentiment_scores table.

Step 3: Calculate Average Sentiment Score

Finally, we can use SQL’s AVG function to calculate the average sentiment score for each record in our dataset. By grouping the records, we can obtain an aggregate sentiment score for the entire dataset or for specific categories:

SELECT
  category,
  AVG(sentiment_score) AS avg_sentiment
FROM
  (
    SELECT
      r.processed_text,
      ss.sentiment_score,
      r.category
    FROM
      reviews r
    JOIN
      sentiment_scores ss ON r.processed_text LIKE '%' || ss.word || '%'
  )
GROUP BY
  category;

In the above query, we group the sentiment scores by the category column to calculate the average sentiment for each category.

Conclusion

Performing sentiment analysis using SQL’s AVG function can be a convenient and scalable approach when dealing with text data directly in your database. By following the steps outlined in this tutorial, you can preprocess text data, assign sentiment scores to words, and calculate average sentiment scores using SQL.

Remember to fine-tune the sentiment scores and preprocess the text data according to your specific application and domain.