Text Analysis
It is all about parsing texts and extracting machine-readable facts from the text when it comes to text analysis. One of the goals of text analysis is to generate organised data from unstructured textual input. The technique can be conceived as slicing and dicing a large number of unstructured, diverse documents into smaller data pieces that are easier to manage and analyse. Approximately 80% of all information is unstructured, with text being one of the most prevalent unstructured data types. Text is also one of the most common types of unstructured data. Because of the messy nature of the text, analysing, comprehending, organising, and sorting through text data is difficult and time-consuming. As a result, most businesses cannot value text information.
Solutions
UGC Moderation
This is a strategy for determining if text data is an advertisement, obscene, out of context, nonsense language, and sentence formation is correct or incorrect.
Document Similarity
Document similarity is an important approach in natural language processing (NLP), and it is used to determine the similarity between two chunks of text based on its meaning or surface.
Resume Parser
Using the Resume Parser, human resource professionals can now significantly speed up the candidate search process by filtering out relevant resumes and creating job descriptions that are both bias-proof and gender-neutral.
Process/Tech Stack
- Extracting data from unstructured data sources such as PDFs and converting it to text format using pdf parsing libraries, web scraping libraries such as lovely soup, and then converting it to JSON or any intermediate format is possible.
- Detecting and removing anomalies from data through natural language processing or other rule-based approaches during the pre-processing and cleansing procedures.
- For computers to conduct any machine learning operation, the data must first be translated into a numeric representation. To accomplish such tasks, several word embedding algorithms, such as Bag of Words, TF-IDF, and word2vec, are employed to encode the text input.
Text Classification Applications
Topic Analysis
This text categorization technique detects the most frequently occurring themes or subjects in a text by conducting a topic analysis. It is possible to use a subject classifier to categorize inbound support tickets, product reviews and Net Promoter Score comments, among other sorts of text. To give an example, this product evaluation would be labelled as follows: Ease of Use
Sentiment Analysis
The objectives of sentiment analysis are identifying subjective information in a text and categorising opinions as good, negative, or neutral. You may use it to analyse sentiments on Text through customer service conversations, survey replies, and other places and gain valuable insights into how your customers feel about your company and products.
Language Detection
A language detector is a programme that automatically classifies a text depending on the language in which it appears. This can be handy when it comes to ticket routing. Suppose you work for a multinational corporation. You can route tickets to teams in other countries familiar with the language.
Intent Detection
This classifier detects the intention hidden behind a sentence, allowing you to take fast action on the information it provides. Examples include emails requesting that you unsubscribe from your product or messages expressing an interest in your product, among other things. By categorising them into intentions such as Unsubscribe and Interested in Product, you may take rapid action on the information.