Blueprints for Text Analytics Using Python: Machine Learning-Based Solutions for Common Real World (NLP) Applications

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Publisher ‏ : ‎ O’Reilly Media; 1st edition (January 12, 2021)
Language ‏ : ‎ English
Paperback ‏ : ‎ 422 pages
ISBN-10 ‏ : ‎ 149207408X
ISBN-13 ‏ : ‎ 978-1492074083
Item Weight ‏ : ‎ 1.5 pounds
Dimensions ‏ : ‎ 7 x 1 x 9.2 inches

Text analytics using Python is a powerful tool for extracting valuable insights from unstructured text data. In this post, we will explore blueprints for implementing machine learning-based solutions for common real-world Natural Language Processing (NLP) applications.

1. Sentiment Analysis: Sentiment analysis is a common NLP task that involves determining the sentiment (positive, negative, or neutral) expressed in a piece of text. To implement sentiment analysis using Python, you can start by pre-processing the text data (removing stop words, punctuation, and special characters), tokenizing the text, and then applying machine learning algorithms such as Naive Bayes or Support Vector Machines to classify the sentiment of the text.

2. Named Entity Recognition (NER): Named Entity Recognition is another important NLP task that involves identifying and classifying named entities in text data, such as names of people, organizations, and locations. To implement NER using Python, you can use pre-trained NER models like spaCy or train your own NER model using machine learning algorithms such as Conditional Random Fields or Recurrent Neural Networks.

3. Text Classification: Text classification involves categorizing text data into predefined categories or labels. Common applications of text classification include spam detection, topic categorization, and sentiment analysis. To implement text classification using Python, you can use machine learning algorithms such as Logistic Regression, Random Forest, or Neural Networks to train a classifier on labeled text data.

4. Text Summarization: Text summarization is the task of generating a concise summary of a longer piece of text. There are two main approaches to text summarization: extractive summarization, which involves selecting and rearranging sentences from the original text, and abstractive summarization, which involves generating new sentences to summarize the text. To implement text summarization using Python, you can use pre-trained models like BERT or GPT-3 for abstractive summarization or implement algorithms like TextRank for extractive summarization.

In conclusion, text analytics using Python offers a wide range of machine learning-based solutions for common real-world NLP applications. By leveraging the power of machine learning algorithms and Python libraries such as NLTK, spaCy, and scikit-learn, you can build robust and scalable text analytics solutions to extract valuable insights from text data.
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