JavaScript: AJAX Fetch HTML Page (Google Sheet), Display As Collapsible List

Library used: 1) jQuery
<script type="text/javascript" src="//code.jquery.com/jquery-1.10.2.js"></script> <style> .collapsibleHeader { background-color: lightgrey; color: black; cursor: pointer; padding: 18px; width: 100%; border: none; text-align: left; outline: none; font-size: 15px } .active, .collapsibleHeader:hover { background-color: lightgrey } .collapsibleContent { padding: 0 18px; display: none; overflow: hidden; background-color: #f1f1f1 } </style> <div id="dynamicNestedList"> </div> <script> function createNestedList(gurl) { $.ajax({ type: "GET", url: gurl, success: function(result) { var data = result; var outputHtml = ""; var docTitle = ($(data).find("#doc-title").text()); var row = ($(data).find("tbody").children()); outputHtml += '<p><a href=' + gurl + ' target="_blank">' + docTitle + '</a></p>'; outputHtml += '<div id="divCollapsible"><ol>'; for (r = 0; r < row.length; r++) { var col = row.eq(r).find("td"); if (col.eq(0).text() === "" || col.eq(0).text() === "title") { continue; } else { outputHtml += '<li class="collapsibleHeader"><b>' + col.eq(0).text() + '</b><br/>'; outputHtml += '<small>' + col.eq(1).text() + "|" + col.eq(2).text() + '<br/>'; outputHtml += '<em>Keywords:' + col.eq(3).text() + '</em></small>'; outputHtml += '</li></li>'; outputHtml += '<div class="collapsibleContent"><ul>'; for (c = 4; c < col.length; c++) { if (col.eq(c).text() === "") { continue; } if (col.eq(c).text().substring(0, 8) === "https://") { outputHtml += "<li><a href=" + col.eq(c).text() + ">"; outputHtml += col.eq(c).text(); outputHtml += "</a></li>" } else { outputHtml += "<li>" + col.eq(c).html() + "</li>"; } } outputHtml += '</ul></div>'; } }; console.log("outputHtml:", outputHtml); $("#dynamicNestedList").append(outputHtml); $(".collapsibleHeader").click(function() { this.classList.toggle("active"); var content = this.nextElementSibling; if (content.style.display === "block") { content.style.display = "none"; } else { content.style.display = "block"; } }); } }); } $(document).ready(function() { var gurl = 'https://docs.google.com/spreadsheets/d/e/2PACX-1vS5dGD3GBffPxTu3z3d3cCZHEMnZPNSw1-l0z-yhwmrwwPIY-iCtEV3rMfWnPzBG5OkVw40l8O5lK9Z/pubhtml?gid=0&single=true'; createNestedList(gurl); }); </script>
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Apps Script Example: doGet doPost fetchUrl

 

/* --------------------------- */ function doGet(e){ if (e.parameter.payload) { //assuming json-formatted data var parsedData=JSON.parse(e.parameter.payload); return outputData(parsedData); }; return outputData(99) } function doPost(e){ if (e.postData) { //assuming json-formatted data var parsedData=JSON.parse(e.postData.contents); return outputData(parsedData); }; return outputData(9) } /* --------------------------- */ function outputData(data){ return ContentService.createTextOutput(JSON.stringify( { "wappstus": "success", "wappdata": data } )).setMimeType(ContentService.MimeType.JSON); } /* --------------------------- */ function postData(){ var objtData = { "name" : "Me Testing", "slug" : "me-testing", "text_to_subscribe" : "true" }; var payload = JSON.stringify(objtData); var options = { "method" : "POST", "contentType" : "application/json", "payload" : payload }; //var url = "https://postman-echo.com/post"; var url = ScriptApp.getService().getUrl(); var textJson = UrlFetchApp.fetch(url, options).getContentText(); Logger.log(textJson); var objtJson = JSON.parse(textJson); Logger.log(objtJson); } /* --------------------------- */ function getData(){ var objtData = { "name" : "Me Testing", "slug" : "me-testing", "text_to_subscribe" : "true" }; var options = { "method" : "GET", "contentType" : "application/json" }; //send object data as encoded string //var url="https://postman-echo.com/get?payload="+encodeURIComponent(JSON.stringify(objtData)); var url = ScriptApp.getService().getUrl()+"?payload="+encodeURIComponent(JSON.stringify(objtData)); var textJson = UrlFetchApp.fetch(url, options).getContentText(); Logger.log(textJson); var objtJson = JSON.parse(textJson); Logger.log(objtJson); } /* --------------------------- */ . . . Read More

How To: Get JSON object from URL

 

Question:

How To: Get JSON object from URL

Answer:

https://stackoverflow.com/questions/15617512/get-json-object-from-url

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How To: Parse JSON into a html table using PHP

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Question: How can I parse JSON into a html table using PHP?

Answer:

https://stackoverflow.com/questions/26213049/how-can-i-parse-json-into-a-html-table-using-php/26213455

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Error (Python) ModuleNotFoundError: No module named 'stemmer'

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Error: 

ModuleNotFoundError: No module named 'stemmer'

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Solution:

Run ...
pip install PyStemmer

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https://stackoverflow.com/questions/49634536/python-stemmer-not-found

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Design your own sentiment analysis tool

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While there are a lot of tools that will automatically give us a sentiment of a piece of text, we learned that they don't always agree! Let's design our own to see both how these tools work internally, along with how we can test them to see how well they might perform.

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https://colab.research.google.com/github/littlecolumns/ds4j-notebooks/blob/master/sentiment-analysis-is-bad/notebooks/Designing%20your%20own%20sentiment%20analysis%20tool.ipynb

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Error (Python, Windows) 'wget' is not recognized as an internal or external command

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‘wget’ is not recognized as an internal or external command – Windows Fix

If you get the following error on windows terminal (CMD / Command Prompt)

‘wget’ is not recognized as an internal or external command,

operable program or batch file.

Or in Git Bash sh: wget: command not found

What this simply means is, wget isn’t installed in your windows machine or it is, but hasn’t been added to Windows environmental path.

If you don’t have wget installed, download it from  here (32-bit) and here (64-bit).

Extract the files to a folder say C:\wget and then add the folder to Windows environmental path.

In some windows machines, the wget command will not be available until after a PC restart.

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How to Build & Release Flutter app for iOS

 

TUTORIAL:

https://www.dripcoding.com/flutter-release-ios-app/

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Flutter Authentication Using Google Sign-In & Firebase

 

TUTORIAL:

https://blog.codemagic.io/firebase-authentication-google-sign-in-using-flutter/

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Building Flutter applications online

 

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Build Apps Effortlessly

Building Flutter applications has never been easier.

NEXT STEP:

https://flutterflow.io/index.html

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Complete Flutter Tutorial From Start Until Publishing To App Store

 

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TUTORIAL:

https://proandroiddev.com/a-new-flutter-app-from-flutter-create-to-the-app-store-e6c2dee17c1a

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Flutter With Firebase Authentication

 

TUTORIAL:

• https://flutlab.io/
• https://console.firebase.google.com/project/flutt1-b87f7/overview

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Download Keystore Maker Software

 

KeyStore Explorer

KeyStore Explorer is an open source GUI replacement for the Java command-line utilities keytool and jarsigner. KeyStore Explorer presents their functionality, and more, via an intuitive graphical user interface.

DOWNLOAD:

https://keystore-explorer.org/index.html

TUTORIAL:

https://headjack.io/tutorial/create-android-keystore-file/

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Install NRC Lexicon from within Jupyter Notebook

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1.Upgrade Python version (if needed)

import sys !{sys.executable} -m pip install --upgrade pip

Expected output:

Requirement already satisfied: pip in c:\portable\jupyterlab-portable-3.1.0-3.9\apps\lib\site-packages (21.2.1) Collecting pip Downloading pip-21.2.4-py3-none-any.whl (1.6 MB) Installing collected packages: pip Attempting uninstall: pip Found existing installation: pip 21.2.1 Uninstalling pip-21.2.1: Successfully uninstalled pip-21.2.1 Successfully installed pip-21.2.4

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2.Install NRC Lexicon

!{sys.executable} -m pip install NRCLex

Expected Output

Collecting NRCLex Downloading NRCLex-3.0.0.tar.gz (396 kB) Collecting textblob Downloading textblob-0.15.3-py2.py3-none-any.whl (636 kB) Collecting nltk>=3.1 Downloading nltk-3.6.3-py3-none-any.whl (1.5 MB) Collecting joblib Downloading joblib-1.0.1-py3-none-any.whl (303 kB) Collecting regex Downloading regex-2021.8.28-cp39-cp39-win_amd64.whl (271 kB) Collecting click Downloading click-8.0.1-py3-none-any.whl (97 kB) Collecting tqdm Downloading tqdm-4.62.3-py2.py3-none-any.whl (76 kB) Requirement already satisfied: colorama in c:\portable\jupyterlab-portable-3.1.0-3.9\apps\lib\site-packages (from click->nltk>=3.1->textblob->NRCLex) (0.4.4) Using legacy 'setup.py install' for NRCLex, since package 'wheel' is not installed. Installing collected packages: tqdm, regex, joblib, click, nltk, textblob, NRCLex Running setup.py install for NRCLex: started Running setup.py install for NRCLex: finished with status 'done' Successfully installed NRCLex-3.0.0 click-8.0.1 joblib-1.0.1 nltk-3.6.3 regex-2021.8.28 textblob-0.15.3 tqdm-4.62.3

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Note: Even after the installation of NRC Lexicon, MissingCorpusError may occur while running programs. So it is recommended to also install textblob.download_corpora.

3.Install textblob.download_corpora (Recommended)

import sys !{sys.executable} -m textblob.download_corpora

Expected Output

[nltk_data] Downloading package brown to C:\Portable\JupyterLab- [nltk_data] Portable-3.1.0-3.9\apps\nltk_data... [nltk_data] Unzipping corpora\brown.zip. [nltk_data] Downloading package punkt to C:\Portable\JupyterLab- [nltk_data] Portable-3.1.0-3.9\apps\nltk_data... [nltk_data] Unzipping tokenizers\punkt.zip. [nltk_data] Downloading package wordnet to C:\Portable\JupyterLab- [nltk_data] Portable-3.1.0-3.9\apps\nltk_data... [nltk_data] Unzipping corpora\wordnet.zip. [nltk_data] Downloading package averaged_perceptron_tagger to [nltk_data] C:\Portable\JupyterLab- [nltk_data] Portable-3.1.0-3.9\apps\nltk_data... [nltk_data] Unzipping taggers\averaged_perceptron_tagger.zip. [nltk_data] Downloading package conll2000 to C:\Portable\JupyterLab- [nltk_data] Portable-3.1.0-3.9\apps\nltk_data... [nltk_data] Unzipping corpora\conll2000.zip. [nltk_data] Downloading package movie_reviews to [nltk_data] C:\Portable\JupyterLab- [nltk_data] Portable-3.1.0-3.9\apps\nltk_data... [nltk_data] Unzipping corpora\movie_reviews.zip.

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Load Dataset Directly into Colab from anywhere on the Browser using CurlWget Extension

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Introduction

CurlWget Extension

Uploading data on Colab from any Website

Dealing with various file formats in Collaboratory

Save and reuse files without wasting the internet

If files are shared with you on gdrive

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https://www.analyticsvidhya.com/blog/2021/08/load-dataset-directly-into-colab-from-anywhere-on-the-browser-using-curlwget-extension/

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Text Processing in Python Text using NLTK and spaCy

  

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The Internet has connected the world, while Social Media like Facebook, Twitter and Reddit provided the platform for people to express their opinions and feelings toward a topic. Then, the proliferation of smartphones increased the usage of these platforms directly. For instance, there are 96% or 2,240 million Facebook active users who used Facebook by smartphones and tablets [1].

The increment in the usage of Social Media has grown the size of text data, and boost the studies or researches in Natural Language Processing (NLP), for example, Information Retrieval and Sentiment Analysis. Most of the time, the documents or the text files to be analyzed are gigantic and contains a lot of noise, directly used raw texts for analysis is inapplicable. Hence, text processing is essential to provide clean input for modelling and analysis.

Text processing contains two main phases, which are tokenization and normalization [2]. Tokenization is the process of splitting a longer string of text into smaller pieces, or tokens [3]. Normalization referring to convert number to their word equivalent, remove punctuation, convert all text to the same case, remove stopwords, remove noise, lemmatizing and stemming.

Stemming — removing affixes (suffixed, prefixes, infixes, circumfixes), For example, running to run

Lemmatization — capture canonical form based on a word’s lemma. For example, better to good [4]

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Text Processing with NLTK

0. Import all needed libraries

1. Tokenization

2. Normalization

a. Removing Stop words

b. Lemmatization

3. Obtain the Cleaned Tokens

Text Processing with spaCy

1. Tokenization + Lemmatization

2. Normalization

a. Removing Noise

b. Removing Stopwords

3. Obtain the Cleaned Tokens

FULL:

https://towardsdatascience.com/text-processing-in-python-29e86ea4114c

References:

[1] M. Iqbal, “Facebook Revenue and Usage Statistics (2020),” 8 March 2021. [Online]. Available: https://www.businessofapps.com/data/facebook-statistics/.

[2] M. Mayo, “A General Approach to Preprocessing Text Data,” 2017. [Online]. Available: https://www.kdnuggets.com/2017/12/general-approach-preprocessing-text-data.html. [Accessed 12 June 2020].

[3] D. Subramanian, “Text Mining in Python: Steps and Examples,” 22 August 2019. [Online]. Available: https://medium.com/towards-artificial-intelligence/text-mining-in-python-steps-and-examples-78b3f8fd913b. [Accessed 12 June 2020].

[4] M. Mayo, “Natural Language Processing Key Terms, Explained,” 2017. [Online]. Available: https://www.kdnuggets.com/2017/02/natural-language-processing-key-terms-explained.html.

[5] “Natural Language Processing In Julia (Text Analysis),” JCharisTech, 1 May 2018. [Online]. Available: https://jcharistech.wordpress.com/2018/05/01/natural-language-processing-in-julia-text-analysis/.

[6] D. Jurafsky and J. H. Martin, “Speech and Language Processing,” 3 December 2020. [Online]. Available: https://web.stanford.edu/~jurafsky/slp3/.

[7] M.F. Goh, “Text Normalization with spaCy and NLTK,” 29 November 2020. [Online]. Available: https://towardsdatascience.com/text-normalization-with-spacy-and-nltk-1302ff430119.

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Sentiment Analysis- Lexicon Models vs Machine Learning

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Why are we even interested in Sentiment Analysis? Well, here’s situation to help you understand.

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https://medium.com/nerd-for-tech/sentiment-analysis-lexicon-models-vs-machine-learning-b6e3af8fe746

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