How Text Analytics Work?
Text Analytics process typically includes several key steps, such as language identification, tokenization, sentence breaking, part-of-speech tagging, chunking, syntax parsing, and sentence chaining. Let’s briefly explore each of these steps:
Steps of Text Analytics Process
Language Identification
- Objective: Determine the language in which the text is written.
- How it works: Algorithms analyze patterns within the text to identify the language. This is essential for subsequent processing steps, as different languages may have different rules and structures.
Tokenization
- Objective: Divide the text into individual units, often words or sub-word units (tokens).
- How it works: Tokenization breaks down the text into meaningful units, making it easier to analyze and process. It involves identifying word boundaries and handling punctuation.
Sentence Breaking
- Objective: Identify and separate individual sentences in the text.
- How it works: Algorithms analyze the text to determine where one sentence ends and another begins. This is crucial for tasks that require understanding the context of sentences.
Part of Speech Tagging
- Objective: Assign a grammatical category (part of speech) to each token in a sentence.
- How it works: Machine learning models or rule-based systems analyze the context and relationships between words to assign appropriate part-of-speech tags (e.g., noun, verb, adjective) to each token.
Chunking
- Objective: Identify and group related words (tokens) together, often based on the part-of-speech tags.
- How it works: Chunking helps in identifying phrases or meaningful chunks within a sentence. This step is useful for extracting information about specific entities or relationships between words.
Syntax Parsing
- Objective: Analyze the grammatical structure of sentences to understand relationships between words.
- How it works: Syntax parsing involves creating a syntactic tree that represents the grammatical structure of a sentence. This tree helps in understanding the syntactic relationships and dependencies between words.
Sentence Chaining
- Objective: Connect and understand the relationships between multiple sentences.
- How it works: Algorithms analyze the content and context of different sentences to establish connections or dependencies between them. This step is crucial for tasks that require a broader understanding of the text, such as summarization or document-level sentiment analysis.
Overall, text analytics involves a combination of linguistic rules, machine learning models, and statistical techniques to extract valuable information from text data. The specific techniques and tools used may vary depending on the application and the complexity of the text analysis task.
What is Text Analytics ?
In a world filled with words, from social media posts to online reviews, understanding what they mean on a large scale is no easy task. That’s where text analytics comes in—a powerful tool that helps us make sense of all this information.
In this article, we’ll take a closer look at text analytics, breaking down what it is, Understanding How it works, its applications, and Techniques.
Table of Content
- What is Text Analytics?
- Why is Text Analytics Important?
- What Text Analytics Can Do?
- How Text Analytics Work?
- Language Identification
- Tokenization
- Sentence Breaking
- Part of Speech Tagging
- Chunking
- Syntax Parsing
- Sentence Chaining
- Various Text Analytics Techniques
- Application of Text Analysis
- The Impact of Text Analytics
- The Future of Text Analytics