Text Analysis Tools And Its 5 Common Techniques
Today, the level of unstructured data is increasing at an alarming rate and handling it has become a pressing need. This has increased demand for technology that can easily tackle this problem speedily and accurately, which is where text analysis comes into the picture.
Often used interchangeably with text mining, text analysis is the process of slicing the heaps of unstructured data into manageable pieces of information. It is the computational process of using various tools and techniques for analyzing text.
Now let’s talk about the 5 techniques which are used in these tools:
- Information Extraction: Information extraction is the process of extracting specific information from textual sources. In short, it is the process of automatically extracting structured data from unstructured data into machine-readable language. The main objective of information extraction is the evaluation of unstructured data to plough out meaningful information from it. It uses the technique of pattern matches to identify the relationship between two elements along with identifying the attributes and key phrases.
- Categorization: This technique involves creating categories and assigning one or more categories to the unstructured text. It is based on the Input–Output principle wherein inputs (associated with the respective category) are fed into the system and the data is classified into new categories after processing.
- Clustering: This is the process of grouping specific data/documents of the similar genre into one particular group. The content of the documents which are placed into a particular cluster are similar to a certain extent. Clustering works on the principle of semantics and use the K algorithm (Keywords Algorithm).
- Visualization: In order to interpret the discovered content, it has to be presented visually. This process involves the enhancement of the content and its presentation by using visualizing cues. It also enables the user to zoom in or zoom out, add various visual cues such as text flags, different colors, etc. The main objective of visualization is to present the content into an appealing and understandable visual hierarchy.
- Summarization: Generation of summarized text with information that is of utmost importance to the user. This involves the major three steps of Pre-Processing, Processing, and Development. Pre-Processing involves constructing the structured representation of data whereas Processing involves the application of the algorithm to generate summarized data and the last step involves the generation of summarized data.
So if you are looking for text analytics software for your business, then tools like Provalis Research text analytics software should be your go-to option.