In the text analytics industry, Graph Analytics is the process of understanding relationships between entities in a text corpus through the use of graph theory. This can be done in order to better understand the structure of the text corpus or to find specific patterns within the data.
There are key differences that set Graph Analytics apart. First, Graph Analytics does not rely on predefined categories or topics. Instead, it looks at the data as a whole and tries to find relationships between entities. Second, Graph Analytics can be used to find both directed and undirected relationships. This means that it can be used to find relationships between entities that are not explicitly connected, as well as to identify the direction of a relationship.
Graph analytics use data points like word co-occurrence, part-of-speech, and proximity measures to understand the relationships between terms in a text corpus. Graph analytics can also be used to find important patterns within the data, such as communities or cliques, or to build predictive models. Additionally, graph analytics can be used to improve the accuracy of other text analytics methods.
Data points and Relationships Used in Graph Analytics :
- Word co-occurrence: This looks at how often two words appear together in a text corpus. This can be used to find relationships between terms.
- Part-of-speech: This looks at the function of a word in a sentence. This can be used to find relationships between terms.
- Proximity measures: This looks at the distance between two words in a text corpus. This can be used to find relationships between terms.
Graph Analytics and Data Science
Graph analytics can be used to build predictive models.
In data science, a model is a mathematical representation of a real-world process. Models can be used to make predictions about future events. Graph analytics can be used to build predictive models by finding patterns in data that can be used to make predictions.
For example, let’s say we have a text corpus that contains information about car accidents. We can use graph analytics to find patterns in this data that can be used to predict where and when car accidents are likely to occur.