Go For Data Science and Natural Language Processing (Golang)

Go for data science and natural language processing in Golang teaches handling strings, tokenization, part-of-speech tagging, sentiment analysis, collocation, and summarization, plus building nlp-powered web apps with Go packages.

Apply regular expressions in Go to clean text for NLP by normalizing case, removing noise such as emails and phone numbers, and preparing data for tokenization.

Explore language detection in Go by installing and comparing two packages, using Getline and another library, to identify languages like English and French with confidence scores.

Go for data science and natural language processing demonstrates tokenization in Golang using regex or an NLP package, choosing treebank or simple tokenizers, and printing the tokenized output.

Explore tokenization in Go for natural language processing using regular expressions and string methods, including word, sentence, and character tokenization and pattern-based splitting.

Explore how to perform named entity recognition chunks in Golang using custom tagging, chunking, and regex patterns, extracting entities like Jesse and London from text.

Explore natural language processing in Go using the Prose package, covering tokenization, part-of-speech tagging, and named entity recognition, with file-based input and practical examples.

This lecture demonstrates a simple Go-based text summarization workflow using the tl;dr approach, comparing attractive and instructive formats and implementing a graph-based ranking method.

Explore sentiment analysis in Go with a rule-based Vader approach using a lexicon of positive and negative words to label text as positive, negative, or neutral and compute compound score.

Create a Go mini project for sentiment analysis, loading a labeled dataset, applying a lexicon-based sentiment analyzer, computing positive, negative, neutral, and compound scores, and storing results for evaluation.

Learn to perform statistics in Golang using standard and external packages, computing mean, min, max, mode, standard deviation, variance, and arithmetic, geometric, and harmonic means.

Explore Gonum for Go data science, using its statistics, distributions, sampling, and matrix tools to perform vector and matrix operations, optimization, and basic data analysis.

Learn to evaluate a Go-based machine learning model with a confusion matrix, computing true positives, true negatives, false positives, and false negatives to derive precision, recall, sensitivity, specificity, and accuracy.

Explore building data web apps in Go using frameworks like Gin, Iris, Fiber, Beego, Revel, Tango, and Buffalo; apply natural language processing to add emotion-aware features and practical tooling.

Explore form handling with the post method, sending user details from the front end to the back end, receiving and rendering results, and improving security over the get method.

Explore how to upload and receive files with GoFiber, detailing front-end to back-end file transfer, form file handling, post requests, encryption, and saving uploaded files to a directory.

Explore how to integrate Bootstrap with GoFiber by importing Bootstrap assets, configuring static and public folders, and rendering templates with Bootstrap components like a container and a Jumbotron.

Learn to build a simple natural language processing web app using a workflow for sentiment analysis, entity and keyword extraction, with GUI and API options for testing.

Enhance a sentiment analysis web app by using bootstrap to beautify the interface, incorporating forms, buttons, and a responsive layout to improve usability and visuals.