Oracle Text enables you to perform sentiment analysis for a topic or document by using sentiment classifiers that are trained to identify sentiment metadata.

With growing amounts of data, organizations must gain more insights about their data rather than just obtaining hits in response to a search query. The insight could be in the form of answering certain basic types of queries (such as weather queries or queries about recent events) or providing opinions about user-specified topics. Keyword searches provide a list of results containing the search term. However, to identify a sentiment or opinion about the search term, must browse through the results and then manually locate the required sentiment information. Sentiment analysis provides a one-step process to identify sentiment information within a set of documents.

Sentiment analysis is the process of identifying and extracting sentiment metadata about a specified topic or entity from a set of documents. Trained sentiment classifiers identify the sentiment. When you run a query with sentiment analysis, in addition to the search results, sentiment metadata is also identified and displayed. Sentiment analysis provides answers to questions such as “Is a product review positive or negative?” or “Is the customer satisfied or dissatisfied?” For example, from a document set consisting of multiple reviews for a particular product, you can determine an overall sentiment that indicates if the product is good or bad.

A sentiment classifier is a type of document classifier that is used to extract sentiment metadata about a topic or document.

To perform sentiment analysis by using a sentiment classifier, you must first associate a sentiment classifier preference with the sentiment classifier and then train the sentiment classifier.

You can associate user-defined sentiment classifiers with a sentiment classifier preference of type SENTIMENT_CLASSIFIER. A sentiment classifier preference specifies the parameters that are used to train a sentiment classifier. These parameters are defined as attributes of the sentiment classifier preference. You can either create a sentiment classifier preference or use the default CTXSYS.DEFAULT_SENTIMENT_CLASSIFIER. To create a user-defined sentiment classifier preference, use the CTX_DDL.CREATE_PREFERENCE procedure to define a sentiment classifier preference and the CTX_DDL.SET_ATTRIBUTE procedure to define its parameters.

To train a sentiment classifier, you need to provide an associated sentiment classifier preference, a training set of documents, and the sentiment categories. If you do not specify a classifier preference, then Oracle Text uses default values for the training parameters. You train the sentiment classifier by using the set of sample documents and the specified preference. You assign each sample document to a category. Oracle Text uses this sentiment classifier to deduce a set of classification rules that define how sentiment analysis must be performed. Use the CTX_CLS.SA_TRAIN procedure to train a sentiment classifier.

Typically, you define and train separate sentiment classifiers for different categories of documents, such as finance, product reviews, and music. If you do not want to create your own sentiment classifier or if suitable training data is not available to train your classifier, you can use the default sentiment classifier provided by Oracle Text. The default sentiment classifier is unsupervised.

To perform sentiment analysis, you run a sentiment query that includes the sentiment classifier which must be used to identify sentiment information. The classifier can be the default or a user-defined sentiment classifier.

You can perform sentiment analysis only as part of a search operation. Oracle Text searches for the specified keywords and generates a result set. Then, sentiment analysis is performed on the result set to identify a sentiment score for each result. If you do not explicitly specify a sentiment classifier in your query, the default classifier is used.

You can either identify one single sentiment for the entire document or separate sentiments for each topic within a document. Most often, a document contains multiple topics and the author’s sentiment toward each topic may be different. In such cases, document-level sentiment scores may not be useful because they cannot identify sentiment scores associated with different topics in the document. Identifying topic-level sentiment scores provides the required answers. For example, when searching through a set of documents containing reviews for a camera, a document-level sentiment tells you whether the camera is good or not. Assume that you want the general opinion about the picture quality of a camera. Performing a topic-level sentiment analysis, with “picture quality” as one of the topics provides the required information.

Oracle Text supports multiple interfaces for performing sentiment analysis.

Use one of the following interfaces to run a sentiment query:

• Procedures in the CTX_DOC package

• XML Query Result Set Interface (RSI)