Almost all type of institutions, organizations, and business industries are storing their data electronically. A huge amount of text is flowing over the internet in the form of digital libraries, repositories, and other textual information such as blogs, social media network and e-mails. It is a challenging task to determine appropriate patterns and trends to extract valuable knowledge from this large volume of data.
Text mining has applications in the exploration of textual content and the transformation of textual information into quantitative variables that can be used in statistical modelling and prediction analysis and also in exploration analysis as filtering or classification parameters. Web content analysis is also a discipline which concerns the extraction of useful information from web documents and exploits data and text mining techniques for the processing and analysis of structured and unstructured records. For the extraction of information from web documents, specialized techniques such as web scaping are used. Our mission in the application of text mining techniques is to capture the information of unstructured data that resides in documents and transform it to meaningful insight.
Extract meaningful information from text according to specific criteria that define the domain of interest
Extract information on text patterns by using a specific set of keywords such as words or phrases
The automatic processing and analysis of unstructured textual information generated from human language.
Create groups of documents which are classified according to the similarity of words or phrases available in the text
The primary aspect of sentiment analysis includes data analysis on the body of the text for understanding the opinion expressed by it and other key factors comprising modality and mood.
- Unlock hidden information
- Analyze the behavior of people
- Analyze customers and competitors to take better decisions
- Spot emerging trends in market
- Targeted identification of information
- Find topics, opinions and ideas
- Predict the feature
- Social media
- Digital Libraries
- Biomedical sciences
- High-Tech technology