Text Analytics
It is estimated that around 80% of all information is unstructured, with the text being one of the most common types of unstructured data. Because of the messy nature of the text, analyzing, understanding, organizing, and sorting through text data is hard and time-consuming so most companies fail to extract value from that.
This is where text analytics with machine learning steps in. By using this, companies can structure business information such as email, legal documents, web pages, chat conversations, and social media messages in a fast and cost-effective way. This allows companies to save time when analyzing text data, help inform business decisions and automate business processes.
Our Solutions
- UGC Moderation: It is a technique to detect whether a text data is advertisement, obscene, out of context, gibberish text and sentence formation is correct or not.
- Document similarity: Document similarity is one of the essential techniques of NLP which is being used to find the closeness between two chunks of text by its meaning or by surface.
- Resume Parser: HR professionals can now considerably speed up candidate search by filtering out relevant resumes and crafting bias-proof and gender-neutral job descriptions.
Process/Tech stack
- Extracting the data from unstructured data sources like PDF converting into text format using pdf parsing libraries like tika, pdf plumber, web scraping libraries like beautiful soup, and converting into JSON or any intermediate format.
- Detecting and removing anomalies from data by conducting pre-processing and cleansing operations using Natural language processing or any rule-based approaches.
- Computers require data to be converted into a numeric format to perform any machine learning task. In order to perform such tasks, various word embedding techniques are being used i.e., Bag of Words, TF-IDF, word2vec to encode the text data.
- Based on the use case, applying text-mining or machine learning approaches to extracting meaningful information from text data. Deploy the model on an endpoint or do UI integration.
Analyze Survey Results
Draw insights from customer and employee survey results by processing the raw text responses using Sentiment Analysis. Aggregate the findings for analysis, follow up, and driving engagements.
Monitor your product's social media feeds
Monitor user product feedback on your product’s twitter or Facebook page. Use the data to analyze customer sentiment toward new products launches, extract key phrases about features and feature requests, or address customer complaints as they happen.
