In the era of Big Data, a significant percentage of the data comes in unstructured text format, coming from various sources like emails, social media, reviews, and more. Extracting valuable insights from this textual data is where text mining comes in, and Oracle Autonomous Database with Oracle Machine Learning (OML) offers a useful platform for such tasks. In this article, we will explore how to implement text mining capabilities in Oracle Autonomous Database using OML.
Understanding Text Mining
Text mining involves extracting valuable information from unstructured text data. This process involves several sub-processes like information retrieval, text transformation, and the application of data mining techniques to discover patterns and insights.
Oracle Machine Learning extends Oracle’s Database to provide a rich set of in-database algorithms for text mining, simplifying the process of creating and managing models and enabling data scientists to work directly with data within the database.
Text Mining with Oracle Machine Learning in Oracle Autonomous Database
Before you start with text mining, you need to preprocess the data, which involves steps like tokenization, stemming, and stopword removal. Oracle provides the Oracle Text package for this, which includes features for text indexing, text querying, and text analysis.
Once the text data is preprocessed, you can use OML to apply machine learning algorithms for various text mining tasks:
- Data Preparation: Before applying any machine learning algorithm, the data needs to be prepared. Oracle Text provides a set of PL/SQL packages for this. For instance, the CTX_DOC package can be used to tokenize the text, filter out stop words, and perform stemming.
- Feature Extraction: The next step is to convert the preprocessed text data into a format that can be fed into a machine learning algorithm. The CTX_DOC.FILTER function can be used to convert the text into a document-term matrix, which is a mathematical matrix that describes the frequency of terms in a document.
- Model Building: After feature extraction, a machine learning model can be built using OML. The OML package provides several in-database algorithms like Support Vector Machines (SVM), Decision Trees, and Naïve Bayes, which can be used for text classification tasks.
- Model Evaluation and Deployment: After building the model, it needs to be evaluated for its performance. OML provides functions to compute various metrics like accuracy, precision, recall, and F1 score. Once the model is evaluated and fine-tuned, it can be deployed for scoring new data.
Case Study: Sentiment Analysis
A common use case for text mining is sentiment analysis. In this task, the aim is to determine the sentiment expressed in a piece of text. This can be particularly useful for businesses to analyze customer feedback, social media comments, or product reviews.
The process starts with data preparation, where the text is cleaned, and irrelevant information is removed. Next, feature extraction is performed to transform the text into a format suitable for machine learning algorithms. Then, a classification algorithm like Naïve Bayes or SVM is applied to train a sentiment analysis model. The trained model can then classify new pieces of text as expressing positive, negative, or neutral sentiment.
Imagine a company, a large online bookstore, that collects user reviews for the books sold on their platform. They want to perform sentiment analysis on these reviews to better understand their customers’ preferences and improve their services.
Data Collection and Preparation
They first user reviews and ratings, and then prepares the data for analysis. This includes cleaning up the text, removing any non-textual content, and irrelevant information.
Oracle Text’s CTX_DOC package can be used for these preprocessing tasks. For instance, CTX_DOC.TOKENIZE function can break down the text into individual words, or “tokens”. It can filter out stop words – common words like ‘and’, ‘is’, ‘the’, etc., that do not add much value for analysis. This package can also perform stemming, which reduces words to their root form (for example, ‘running’ becomes ‘run’).
After the data preparation step, this company needs to convert the cleaned-up text data into a format that can be fed into a machine learning algorithm. This involves creating a document-term matrix, which records the frequency of terms in each document.
Oracle’s CTX_DOC.FILTER function can help with this, transforming the text into a structured format that machine learning algorithms can process.
Now, the company is ready to build the machine learning model. They decide to use a Naïve Bayes classifier, a popular algorithm for text classification tasks, which is available through the DBMS_DATA_MINING package in OML.
In this step, the prepared data is divided into two sets: a training set, which the model learns from, and a test set, which is used to evaluate the model’s performance.
The model is trained using the training set, where it learns to associate certain terms with positive, negative, or neutral sentiments based on the user ratings associated with the reviews.
Model Evaluation and Deployment
After building the model, it’s time to evaluate its performance. This is done using the test set – the model attempts to predict the sentiment of the reviews in the test set, and these predictions are compared with the actual sentiments (based on user ratings). Performance metrics like accuracy, precision, and recall can be calculated using functions provided by OML.
Once satisfied with the model’s performance, this company can deploy it for scoring new data. As new reviews come in, they can be fed into the model to predict their sentiment. This enables them to continuously monitor customer sentiment and respond accordingly.
By using Oracle Machine Learning and Oracle Autonomous Database, this company has managed to turn unstructured text data into actionable insights, helping them understand their customers better and continually improve their services.
Oracle Autonomous Database, combined with Oracle Machine Learning, provides an integrated, scalable, and secure platform for performing text mining tasks. By leveraging these capabilities, businesses can uncover valuable insights from their unstructured text data, enabling them to make data-driven decisions and strategies.