Decoding Public Sentiments Towards Energy Transition Using NLP

Getting On Board With The Project

During new year 2020, I made a small vow to find opportunities to work on collaborative projects for data science, machine learning, and AI. I was always transfixed at the work Omdena had achieved in the past and was keeping the website in my bookmarked list for quite some time.

Being a student from an engineering background, I finally applied for the energy transition challenge and was met with a list of objectives along with an opportunity to communicate with some of the finest professionals I’ve ever met. A few days later I received my acceptance and thus began my return to a long untouched world. The challenge concerned the study of social sentiments for topics such as energy transition, sustainability, and the like which would help chart out better policies in the future.

What sets this challenge apart from the rest was the sheer scale of data collected, social channels scraped and data analyzed. The end result were findings so crucial and insightful to the primary objective that the team gained something new to learn and work with.

The Problem: Understanding Energy Transition

The energy transition is a process that needs a great deal of deliberation to figure out which technologies will be the best to satisfy future energy needs, but also how sustainable and environmentally friendly they are. But at the end of the day, it is a matter of public concern as taxpayers vote with their wallets over such issues. As a result, it becomes necessary to gauge public interests and formulate a clearer picture of their thoughts, complaints, and ideas. The challenge set by the WEC for the team was to accomplish this by collecting as many different sources of data and running the algorithms to know what makes the public tick.

Image for post

Image for post

How textual data appears-ripe and ready for processing.

Further discussions produced more defined questions: “What energies are people willing to support?”, “Do people have the same issues globally or are public energy problems dependent on the geography?”, “What are the common factors driving these sentiments?”, “Why are public perceptions too positive or negative in certain channels when compared to others?”, “What are the word associations that people are using when discussing these topics?”

I was primarily focused on scraping sources from Facebook and Reddit which were further analyzed for classifying unsupervised texts. An additional aspect of the analysis also looked at working on energy transition texts from websites, news sources, and all that could be collected. To paraphrase Galileo Galilei,

Measure what is measurable, and make measurable what is not so.

Making A Data Recipe

What’s buzzing among people? It’s data. Crude, multidimensional, and confusing-data sits as the invisible output from our everyday lives, which places it at an important position for conducting analyses.

Needless to say, the next step of this process was to write scripts to collect data from the Facebook Graph API and Reddit channels, dashing them with additional data from news sources, public channels, and whatever could be found. This is where even the most seasoned analyst runs across problems like data parsing, removing unnecessary verbiage, and cleaning documentation to make a useful corpus.

If the data that is fit for analysis is the ‘final dish’, the recipe will undoubtedly look like a mangled mess. Luckily, this is where some of the finer packages for RStudio and Python come in handy. Public Facebook pages for newspapers like ABC News, Fox News, and The Huffington Post, further helped in enriching the dataset.

#sustainability #naturallanguageprocessing #renewable-energy #artificial-intelligence #energy

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Decoding Public Sentiments Towards Energy Transition Using NLP

Decoding Public Sentiments Towards Energy Transition Using NLP

Getting On Board With The Project

During new year 2020, I made a small vow to find opportunities to work on collaborative projects for data science, machine learning, and AI. I was always transfixed at the work Omdena had achieved in the past and was keeping the website in my bookmarked list for quite some time.

Being a student from an engineering background, I finally applied for the energy transition challenge and was met with a list of objectives along with an opportunity to communicate with some of the finest professionals I’ve ever met. A few days later I received my acceptance and thus began my return to a long untouched world. The challenge concerned the study of social sentiments for topics such as energy transition, sustainability, and the like which would help chart out better policies in the future.

What sets this challenge apart from the rest was the sheer scale of data collected, social channels scraped and data analyzed. The end result were findings so crucial and insightful to the primary objective that the team gained something new to learn and work with.

The Problem: Understanding Energy Transition

The energy transition is a process that needs a great deal of deliberation to figure out which technologies will be the best to satisfy future energy needs, but also how sustainable and environmentally friendly they are. But at the end of the day, it is a matter of public concern as taxpayers vote with their wallets over such issues. As a result, it becomes necessary to gauge public interests and formulate a clearer picture of their thoughts, complaints, and ideas. The challenge set by the WEC for the team was to accomplish this by collecting as many different sources of data and running the algorithms to know what makes the public tick.

Image for post

Image for post

How textual data appears-ripe and ready for processing.

Further discussions produced more defined questions: “What energies are people willing to support?”, “Do people have the same issues globally or are public energy problems dependent on the geography?”, “What are the common factors driving these sentiments?”, “Why are public perceptions too positive or negative in certain channels when compared to others?”, “What are the word associations that people are using when discussing these topics?”

I was primarily focused on scraping sources from Facebook and Reddit which were further analyzed for classifying unsupervised texts. An additional aspect of the analysis also looked at working on energy transition texts from websites, news sources, and all that could be collected. To paraphrase Galileo Galilei,

Measure what is measurable, and make measurable what is not so.

Making A Data Recipe

What’s buzzing among people? It’s data. Crude, multidimensional, and confusing-data sits as the invisible output from our everyday lives, which places it at an important position for conducting analyses.

Needless to say, the next step of this process was to write scripts to collect data from the Facebook Graph API and Reddit channels, dashing them with additional data from news sources, public channels, and whatever could be found. This is where even the most seasoned analyst runs across problems like data parsing, removing unnecessary verbiage, and cleaning documentation to make a useful corpus.

If the data that is fit for analysis is the ‘final dish’, the recipe will undoubtedly look like a mangled mess. Luckily, this is where some of the finer packages for RStudio and Python come in handy. Public Facebook pages for newspapers like ABC News, Fox News, and The Huffington Post, further helped in enriching the dataset.

#sustainability #naturallanguageprocessing #renewable-energy #artificial-intelligence #energy

8 Open-Source Tools To Start Your NLP Journey

Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge. NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition techniques along with text simplification.

To address the current requirements of NLP, there are many open-source NLP tools, which are free and flexible enough for developers to customise it according to their needs. Not only these tools will help businesses analyse the required information from the unstructured text but also help in dealing with text analysis problems like classification, word ambiguity, sentiment analysis etc.

Here are eight NLP toolkits, in no particular order, that can help any enthusiast start their journey with Natural language Processing.


Also Read: Deep Learning-Based Text Analysis Tools NLP Enthusiasts Can Use To Parse Text

1| Natural Language Toolkit (NLTK)

About: Natural Language Toolkit aka NLTK is an open-source platform primarily used for Python programming which analyses human language. The platform has been trained on more than 50 corpora and lexical resources, including multilingual WordNet. Along with that, NLTK also includes many text processing libraries which can be used for text classification tokenisation, parsing, and semantic reasoning, to name a few. The platform is vastly used by students, linguists, educators as well as researchers to analyse text and make meaning out of it.


#developers corner #learning nlp #natural language processing #natural language processing tools #nlp #nlp career #nlp tools #open source nlp tools #opensource nlp tools

Why Use WordPress? What Can You Do With WordPress?

Can you use WordPress for anything other than blogging? To your surprise, yes. WordPress is more than just a blogging tool, and it has helped thousands of websites and web applications to thrive. The use of WordPress powers around 40% of online projects, and today in our blog, we would visit some amazing uses of WordPress other than blogging.
What Is The Use Of WordPress?

WordPress is the most popular website platform in the world. It is the first choice of businesses that want to set a feature-rich and dynamic Content Management System. So, if you ask what WordPress is used for, the answer is – everything. It is a super-flexible, feature-rich and secure platform that offers everything to build unique websites and applications. Let’s start knowing them:

1. Multiple Websites Under A Single Installation
WordPress Multisite allows you to develop multiple sites from a single WordPress installation. You can download WordPress and start building websites you want to launch under a single server. Literally speaking, you can handle hundreds of sites from one single dashboard, which now needs applause.
It is a highly efficient platform that allows you to easily run several websites under the same login credentials. One of the best things about WordPress is the themes it has to offer. You can simply download them and plugin for various sites and save space on sites without losing their speed.

2. WordPress Social Network
WordPress can be used for high-end projects such as Social Media Network. If you don’t have the money and patience to hire a coder and invest months in building a feature-rich social media site, go for WordPress. It is one of the most amazing uses of WordPress. Its stunning CMS is unbeatable. And you can build sites as good as Facebook or Reddit etc. It can just make the process a lot easier.
To set up a social media network, you would have to download a WordPress Plugin called BuddyPress. It would allow you to connect a community page with ease and would provide all the necessary features of a community or social media. It has direct messaging, activity stream, user groups, extended profiles, and so much more. You just have to download and configure it.
If BuddyPress doesn’t meet all your needs, don’t give up on your dreams. You can try out WP Symposium or PeepSo. There are also several themes you can use to build a social network.

3. Create A Forum For Your Brand’s Community
Communities are very important for your business. They help you stay in constant connection with your users and consumers. And allow you to turn them into a loyal customer base. Meanwhile, there are many good technologies that can be used for building a community page – the good old WordPress is still the best.
It is the best community development technology. If you want to build your online community, you need to consider all the amazing features you get with WordPress. Plugins such as BB Press is an open-source, template-driven PHP/ MySQL forum software. It is very simple and doesn’t hamper the experience of the website.
Other tools such as wpFoRo and Asgaros Forum are equally good for creating a community blog. They are lightweight tools that are easy to manage and integrate with your WordPress site easily. However, there is only one tiny problem; you need to have some technical knowledge to build a WordPress Community blog page.

4. Shortcodes
Since we gave you a problem in the previous section, we would also give you a perfect solution for it. You might not know to code, but you have shortcodes. Shortcodes help you execute functions without having to code. It is an easy way to build an amazing website, add new features, customize plugins easily. They are short lines of code, and rather than memorizing multiple lines; you can have zero technical knowledge and start building a feature-rich website or application.
There are also plugins like Shortcoder, Shortcodes Ultimate, and the Basics available on WordPress that can be used, and you would not even have to remember the shortcodes.

5. Build Online Stores
If you still think about why to use WordPress, use it to build an online store. You can start selling your goods online and start selling. It is an affordable technology that helps you build a feature-rich eCommerce store with WordPress.
WooCommerce is an extension of WordPress and is one of the most used eCommerce solutions. WooCommerce holds a 28% share of the global market and is one of the best ways to set up an online store. It allows you to build user-friendly and professional online stores and has thousands of free and paid extensions. Moreover as an open-source platform, and you don’t have to pay for the license.
Apart from WooCommerce, there are Easy Digital Downloads, iThemes Exchange, Shopify eCommerce plugin, and so much more available.

6. Security Features
WordPress takes security very seriously. It offers tons of external solutions that help you in safeguarding your WordPress site. While there is no way to ensure 100% security, it provides regular updates with security patches and provides several plugins to help with backups, two-factor authorization, and more.
By choosing hosting providers like WP Engine, you can improve the security of the website. It helps in threat detection, manage patching and updates, and internal security audits for the customers, and so much more.

Read More

#use of wordpress #use wordpress for business website #use wordpress for website #what is use of wordpress #why use wordpress #why use wordpress to build a website

Dominic  Feeney

Dominic Feeney

1622273248

Sentiment Analysis Using TensorFlow Keras - Analytics India Magazine

Natural Language Processing is one of the artificial intelligence tasks performed with natural languages. The word ‘natural’ refers to the languages that evolved naturally among humans for communication. A long-standing goal in artificial intelligence is to make a machine effectively communicate with humans. Language modeling and Language generation (such as neural machine translation) have been popular among researchers for over a decade. For an AI beginner, learning and practicing Natural Language Processing can be initialized with classification of texts. Sentiment Analysis is among the text classification applications in which a given text is classified into a positive class or a negative class (sometimes, a neutral class, too) based on the context. This article discusses sentiment analysis using TensorFlow Keras with the IMDB movie reviews dataset, one of the famous Sentiment Analysis datasets.

TensorFlow’s Keras API offers the complete functionality required to build and execute a deep learning model. This article assumes that the reader is familiar with the basics of deep learning and Recurrent Neural Networks (RNNs). Nevertheless, the following articles may yield a good understanding of deep learning and RNNs:

#developers corner #imdb dataset #keras #lstm #lstm recurrent neural network #natural language processing #nlp #recurrent neural network #rnn #sentiment analysis #sentiment analysis nlp #tensorflow

Nat  Grady

Nat Grady

1660108440

Wordcloud2: R interface to Wordcloud for Data Visualization

wordcloud2

R interface to wordcloud for data visualization. Timdream's wordcloud2.js is used in this package.

Original description

Installation

devtools::install_github("lchiffon/wordcloud2")

knitr and shiny is support in wordcloud2 package.

Example

library(wordcloud2)
wordcloud2(demoFreq, size = 1,shape = 'star')

1

wordcloud2(demoFreq, size = 2, minRotation = -pi/2, maxRotation = -pi/2)

1

wordcloud2(demoFreq, size = 2, minRotation = -pi/6, maxRotation = -pi/6,
  rotateRatio = 1)

1

Chinese version

## Sys.setlocale("LC_CTYPE","eng")
wordcloud2(demoFreqC, size = 2, fontFamily = "微软雅黑",
           color = "random-light", backgroundColor = "grey")

1

Example of successfully deploying interactivate clickable wordcloud with special shape on R-shiny

Thanks JacobXPX's contribution to this feature:

Thanks AdamSpannbauer for pointing out the issues.

Additional features are added or modified:

hover information display are fixed, refering AdeelK93's previous work, thanks!

multiple wordclouds which seperatedly click are supported.

clickedWordInputId is changed to be automatically generated by: paste0(outputId, "_clicked_word")).

See sample below for more details:

library(shiny)
library(wordcloud2)
shinyApp(
  ui=shinyUI(fluidPage(
    #using default clicked word input id
    wordcloud2Output("my_wc", width = "50%", height = "400px"),
    #using custom clicked word input id
    wordcloud2Output("my_wc2", width = "50%", height = "400px"),
    
    verbatimTextOutput("print"),
    verbatimTextOutput("print2")
  )),
  server=shinyServer(function(input,output,session){
    
    figPath = system.file("examples/a.png",package = "wordcloud2")
    
    output$my_wc  = renderWordcloud2(wordcloud2(data = demoFreq, figPath = figPath, size = 0.4,color = "blue"))
    output$my_wc2 = renderWordcloud2(wordcloud2(demoFreq))
    
    #using default clicked word input id
    output$print  = renderPrint(input$my_wc_clicked_word)
    #using custom clicked word input id
    output$print2 = renderPrint(input$my_wc2_clicked_word)
  })
)

run the above code and click refresh, it will work.

1

contributors

Download Details:

Author: Lchiffon
Source Code: https://github.com/Lchiffon/wordcloud2 

#r #datavisualization