Introduction
With each passing second, the amount of data shared and transferred between humans grows exponentially. Organising, analysing, predicting, and making decisions based on such data is daunting. Companies today strive to understand the most recent market trends, customer preferences, and other requirements, which necessitates the interpretation of massive amounts of data as the main asset.
Big Data consists of a large amount of data that cannot be processed by traditional data storage or processing devices. Many multinational organisations use it to process data and conduct business. The data flow would have been 150 exabytes per day before replication.
Analysis and Extraction Techniques
In general, text analytics systems extract information from unstructured data using a combination of statistical and Natural Language Processing (NLP) techniques. NLP is a large and sophisticated field that has grown in popularity over the last two decades. NLP's primary purpose is to extract meaning from text. Linguistic notions such as grammatical structures and components of speech are commonly used in Natural Language Processing. The goal of this type of analysis is usually to figure out who did what to whom, when, where, how, and why.
NLP carries out text analysis at various levels:
- Lexical/morphological analysis looks at the features of a single word, such as prefixes, suffixes, roots, and parts of speech (noun, verb, adjective, etc.), in order to figure out what the word means in the context of the given text.
- Syntactic analysis dissects the text and places individual words in context using grammatical structure.
- A sentence's possible interpretations are determined by semantic analysis.
- The discourse-level analysis aims to determine the meaning of text past the sentence level.
Organisations often need to design rules to extract information from diverse document sources. Organisations can create rules either manually or automatically, or a combination of both:
- In the manual approach, someone creates a set of extraction criteria using a proprietary language. While the manual method is time-consuming, it can yield highly accurate results.
- Machine learning or other statistical techniques may be used in automated processes. Based on a collection of training and text data, the software develops rules. To develop — that is, learn — the rules, the system first processes a series of similar documents (for example, newspaper articles). The user then runs a test data set to see if the rules are accurate.
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