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Natural Language Processing vs Text Analytics

September 23, 2021

Artificial intelligence (AI) has revolutionized the way organizations process, analyze, and interpret vast amounts of data generated daily. Two techniques that have gained significant popularity are natural language processing (NLP) and text analytics. While they may seem similar, there are key differences between the two. In this blog post, we’ll compare NLP and text analytics and identify their pros and cons.

What is Natural Language Processing (NLP)?

NLP is a branch of AI that allows machines to understand human language. In simpler terms, it enables computers to "read," "understand," and "interpret" human language in a way that can be analyzed by humans or other machines. As an example, NLP can take plain text and derive meaning from it or even generate new text that has a similar meaning.

What is Text Analytics?

Text analytics, on the other hand, is a subset of NLP. It involves analyzing and extracting data from unstructured data sources like emails, social media posts, reviews, and customer feedback. It processes textual data using statistical algorithms and machine learning techniques to identify patterns and predict outcomes.

Key differences between NLP and Text Analytics

  • Scope: NLP covers a broad range of applications like translation, sentiment analysis, named entity recognition, and summarization. Text analytics, on the other hand, focuses more on text-based data analysis and mining.
  • Approach: NLP tends to be more context-driven and relies on advanced algorithms like Convolutional Neural Networks (CNNs), Recursive Neural Networks (RNNs), and Generative Adversarial Networks (GANs). Text analytics is generally more statistical and involves techniques like clustering, classification, and regression analysis.
  • Output: NLP generates text-based output, including sentiment scores, summaries, and translations. Text analytics generates numerical output like word frequencies, sentiment distributions, and topic models.

Precision & Accuracy

In terms of precision and accuracy, both NLP and text analytics have their strengths. NLP's accuracy can range from 70% to above 90%, depending on the type of analysis it's performing. In contrast, text analytics can achieve accuracy levels of over 95% for certain types of data analysis.

Pros and Cons

Natural Language Processing (NLP)

Pros:

  • Can recognize sarcasm, irony, and sentiment in text
  • Can summarize long documents to provide a brief overview
  • Can handle complex language syntax

Cons:

  • Can be costly due to the need for training data and advanced algorithms
  • Requires significant computing resources
  • Can be challenging to incorporate into existing systems

Text Analytics

Pros:

  • Can handle large volumes of unstructured data
  • Extracts useful insights and trends from text data
  • Enables businesses to analyze feedback from customers in real-time.

Cons:

  • Generally less accurate than NLP
  • Struggles with sarcasm, irony, and sentiment-based language

Conclusion

Both NLP and text analytics are crucial techniques in the realm of data processing and analysis. While both can be utilized to derive insights, they each have their strengths and weaknesses. NLP is better suited for tasks that require more advanced language-processing abilities, while text analytics is ideal for tasks that involve larger volumes of data. Ultimately, selecting the best tool for your organization's needs involves understanding the data you're dealing with and what insights you're hoping to attain.

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