A lightweight JavaScript library for basic text analysis operations like summarization, sentiment analysis, keyword extraction, and classification.
npm install textmagicTextMagic JS is a lightweight JavaScript library designed to perform basic text analysis operations such as summarization, sentiment analysis, keyword extraction, and text classification. It utilizes simple algorithms such as Naive Bayes and TF-IDF for these tasks.
- Text Summarization: Extracts the most relevant sentences from the text.
- Sentiment Analysis: Detects whether the sentiment of the text is positive, negative, or neutral.
- Keyword Extraction: Identifies the most frequent words in the text.
- Text Classification: Classifies the text into categories like "news", "sports", and "entertainment."
To install the library, run the following command:
``bash`
npm install textmagic-js
Summarize the text by extracting the most relevant sentences.
`javascript
const TextAnalyzer = require('textmagic-js');
const analyzer = new TextAnalyzer();
const text = "This is the first sentence. This is the second sentence. This is the third sentence.";
console.log(analyzer.summarize(text, 2)); // Will return the top 2 sentences.
`
Analyzes the sentiment of the given text and returns the sentiment as positive, negative, or neutral.
`javascript`
const sentiment = analyzer.sentimentAnalysis("I love this! It makes me happy.");
console.log(sentiment); // Will return 'positive'.
Extracts the top 5 keywords from the given text.
`javascript`
const keywords = analyzer.extractKeywords("Artificial Intelligence is a branch of computer science.");
console.log(keywords); // Will return an array of the most frequent words.
Classifies the text into predefined categories such as "news", "sports", or "entertainment" using Naive Bayes classifier.
`javascript`
const category = analyzer.classify("The football match was exciting.");
console.log(category); // Will return the predicted category (e.g., 'sports').
- Text Summarization: Uses the TF-IDF algorithm to determine the most important sentences based on their term frequency.
- Sentiment Analysis: Uses the sentiment` package to analyze the sentiment of the text.
- Keyword Extraction: Tokenizes the text into words and calculates the frequency of each word to determine the most frequent keywords.
- Text Classification: Uses a Naive Bayes classifier to predict the category of the text based on training data.
This library uses basic algorithms and techniques, making it suitable for lightweight applications. However, it may not provide the accuracy of more advanced NLP models powered by machine learning.