The Sentiment Revealer (SentiRe) allows organizations to gather and extract subjective aspects related to the opinions and sentiment of users (writers and or readers) about a product, an idea or a service.
It acts on information sources that are publically available, such as blogs, micro-blogs and forums, in particular characterized by unstructured information, such as free-text messages, HTML pages or texts.
SentiRE allows creating personalized reports out of relatively free large-scale document collections, in support of the decision-making processes in business intelligence scenarios (e.g. banking or brand reputation on the Web). Such reports enhance the information extracted by enabling timeliness in predictive business intelligence processes.
The “hidden” feeling in the message is made explicit using dedicated neural classifiers
It is a distributed and scalable system, based on a service architecture capable of downloading texts and messages from the Web and Social Networks, processing them using advanced Machine Learning tools in order to express contents, feelings and emotions expressed in the written text. This semantic enrichment is automatically performed in combination with other engines from Reveal, in particular RevNLT and Revealer, enabling advanced Business Intelligence and Brand Reputation processes for an effective aggregation and retrieval of information.
A typical workflow of SentiRe is the automatic collection and analysis of messages from Social Networks (such as Twitter). Messages are downloaded, filtered according to topics of interest for the client and their “hidden” feeling is made explicit using dedicated neural classifiers. This is combined with automatic methods of time-based and community analyses in order to answer questions such as “How changed the idea of this community about this topic?”