DEV Community: Sandesh B Suvarna (Iconic.wtf)

How Did I Get 245,466 Views on My Tech Vlog?

Sandesh B Suvarna (Iconic.wtf) — Mon, 14 Sep 2020 19:53:39 +0000

1. Watch out trends on Twitter.

I found out that the below news is trending on Twitter. The storyline was, a software Techgiant had built an app for Airport Security Administration & charged them $1.4 million.

2. Quickly make a hands-on video

I built the app using the Ionic framework & recorded the screen while building it. Later, I uploaded it on Youtube [Excuse me for the bad audio]

How to build a $1.4Million dollar app within 4 minutes - TSA Randomizer

Note: You can use either Loom(Free) or Camtasia(Paid).

3. Write titles people want to click.

When you upload your video to YouTube, ensure to write a catchy headline. You can use a service like title-gen.

4. Reach out to tech bloggers (Email them personally).

The above CNET blog, “You’ll never guess how much the TSA’s Randomizer app cost” was written by “Chris Matyszczyk”. I searched for his email ID & dropped him an email stating that I have a piece of share-worthy news available for him. Chris took an interview over email & published it in a while.

Remember, authors usually lookout for content to create news. That's what their job is. Therefore, your job is to make their life easier by giving them the right content.

5. Reach out to others

Once you get covered on major news media like CNET.com reach out to smaller tech media agencies. Smaller media agencies will right away consider your content as its already been validated by biggies.
My blog got even featured on Chinese media. Luckily, it was all over the world.

& that's it. I hope this helps you get better reach to your tech vlogs.

For more growth hacks follow me on Twitter — @sandeshsuvarna

Behind the Tech: How does Siri, Alexa or Google Home respond to your questions?

Sandesh B Suvarna (Iconic.wtf) — Fri, 16 Nov 2018 12:41:48 +0000

Ever wondered how Apple Siri, Amazon Alexa or Google Home responds to your questions?

How does a computer understand human language which sometimes human themselves do not understand?

Well, technically it is known as Natural Language Processing.

Natural Language Processing (NLP)

The field of study that focuses on the interactions between human language and computers is called Natural Language Processing, or NLP for short. It sits at the intersection of computer science, artificial intelligence, and computational linguistics (Wikipedia).

Behind the tech: NLP (Basics)

Disclaimer: There are multiple techniques to process a Natural Language, I will be shedding light only on some.

Let me tell you an instance about Nidah.

Nidah is 2 yrs old & she doesn’t know any of the languages. But when her mom asks her to drink milk, she drinks it!

So how does Nidah processes the information from the language which she doesn’t know?

Nidah’s mom must have shown her some hand signs or she must have trained Nidha that when she says “Nidah please drink milk”, Nidah has to drink milk. So Nidah understood it, processed it & also stored that information for future use. This entire workflow got finished as Nidah was trained. If her mom would have asked her to do something which Nidah wasn’t aware(trained) of then Nidah wouldn’t have processed the information.

Now Looking into Nidah’s example, there were 2 stages of processing an information

Training Stage (Pre-processing Stage)
Process + Decision-Making Stage

Training Stage

Let me take a look into Nidah’s mom’s statement again.

“Nidah please drink milk”

Question 1: How did Nidah understand what task to perform?
Question 2: How did Nidah understand that she has to drink milk, not coffee?

Simple, while training Nidah you tell her that “drink milk” has a verb “Drink” & has a property “Milk”. Now Nidah knows what task to perform & what to drink. I would call her “Trained Nidah”. (Technically the trained data is called “Data model”)

Similarly, in the field of NLP, the verb “drink” is called “Intent” & the property “milk” is called “Entity” & method of processing the data in this fashion is known as: Named Entity & Intent Recognition.

An Entity is a property which can be used to answer the request from the user — the entity will usually be a keyword within the request such as a name, date, location etc.

An Intent(Intention), is a specific action that the user can invoke such as drink, place an order, book a ticket etc

There are a bunch of NLP tools like Google Dialogflow, Facebooks wit.ai, IBM Watson or Microsoft’s LUIS where you can host your “trained data model”. Let me call it the “BRAIN”. These brains even provide data processing & decision making abilities.

Process + Decision-Making Stage

Processing looks simple from outside but it undergoes a series of a pipeline of text processing sub stages like Stemming and Lemmatizing, TF-IDF, Coreference resolution, Part-of-speech (POS) Tagging, Dependency Parsing, Named Entity/Intent Recognition & many more.

With the evolution of 3 party NLP tools, developers need not worry about knowing many of the processing sub stages as these tools take care of it & make your life easier.

Now going back to the Nidah’s example, if you have already trained “Brain” (3rd party NLP tools) & once the brain receives a message like “Nidah please drink milk”, the brain will process the message & understands that the intent is “to drink” & entity is “Milk”.

But how the brain will know what action needs to be taken?

Well, you can even train the brain based on the intent received that it has to drink milk or utter an acknowledgment “ok”.

Summary:

In the world of NLP, It’s all about how much-structured data(Corpus) you have! More the data more the accuracy. Also as we humans keep updating our language dictionary, training a computer is an endless process.

I hope that this post gives a good initial overview of Natural Language Processing. It is a good exercise to ask yourself questions like:

How will the computer understand sarcasm which even we humans never understand?
Handling synonyms, ambiguity & coreferences?
How would I design an NLP workflow using Google Dialogflow?

What comes next:

Thank you very much for reading until here. In my next blog, I will be covering “How would I design an NLP workflow using Google Dialogflow?”. Stay tuned.

Find “Toxicity/Profanity” of a comment using Google Perspective

Sandesh B Suvarna (Iconic.wtf) — Fri, 16 Nov 2018 12:35:53 +0000

Social media has become a huge part of everyone’s lives. As of 2017, daily social media usage of global internet users amounted to 135 minutes per day. When it comes to extreme averages, teenagers take the prize. Their daily average time spent on social media is nine hours a day. That’s longer than most people spend sleeping or in school.

As we spend increasing amounts of time online, and new technologies allow us to connect further in the virtual world, the further we connect, the further we create online issues. One such major issue is online harassment.

In a survey of over 3,000 Americans, 47% reported experiencing some sort of online harassment or abuse. To curb this issue, one of the steps taken from Google is to come up with a technology which could help improve online conversations. Introducing Google Perspective.

Google Perspective:

Perspective is an API that makes it easier to host better conversations. The API uses machine learning models to score the perceived impact a comment might have on a conversation. Developers and publishers can use this score to give real-time feedback to commenters or help moderators do their job, or allow readers to more easily find relevant information. Perspective identifies whether a comment could be perceived as “toxic” to a discussion.

How to use Google Perspective?

Well, first you need access to Google’s Perspective API. Which can be availed by getting whitelisted by the perspective team. Apply here
Once you get whitelisted, go ahead & create a Google Cloud project.
Search for ‘Perspective API’ from the list of Google API’s, then go to the Perspective API’s overview page and click Enable.
To authenticate your requests, you’ll need to generate credentials for your project. Using an API key is the simplest option. Go to the API credentials page, click Create credentials, and choose “API Key”.

Now you have everything, except a nodejs client to find “Toxicity/Profanity” of a comment using Google Perspective.

Tada…. & here is your open source nodejs client for Google Perspective:
I, open sourced a nodejs client for Google perspective with a clear-cut documentation for a quick start. Click on the below link, use it & feel free to raise an issue if you find any.

sandeshbsuvarna / Comment-Toxicity-Profanity-Analyzer

Comment Toxicity/Profanity Analyzer, will help help increase empathy, participation and quality in online conversation at scale using Google's perspective api

Comment Toxicity/Profanity Analyzer | Nodejs, Google Perspective API

Comment Toxicity/Profanity Analyzer, will help help increase empathy, participation and quality in online conversation at scale. #ConversationAI #GooglePerspectiveAPI

Quick Start

Get whitelisted to use perspective api To use the API, your you need to have your email address whitelisted. Apply here.
Create a Google Cloud project. Go to the Google Cloud console to create a new project You can also use an existing project, if you have one.
Enable the API. Once you've been whitelisted, you'll be able to enable the API in for your project. Go to the Perspective API's overview page and click Enable.
Generate an API key. To authenticate your requests, you'll need to generate credentials for your project. Using an API key is the simplest option. Go to the API credentials page, click Create credentials, and choose "API Key".
Make an AnalyzeComment request…

View on GitHub

                    The Internet Should Be a Good Place

Build Chatbot for Twitter Direct Message

Sandesh B Suvarna (Iconic.wtf) — Sun, 23 Sep 2018 06:18:42 +0000

Step 1: Get Developer account

https://developer.twitter.com/en/apply-for-access
Note: Review & approval usually takes 10–15 days.

Step 2: Create Twitter App & Dev Environment

https://developer.twitter.com/en/account/get-started

Step 3: Generate app access token for the direct message using twitter developer portal

Note: Change the app permissions to "Read, write, and direct messages" & generate the access token.

Step 4: Create the Node module & run it.

Run command: node app.js

Step 5: Tunnel to your localhost webhook using Ngrok

run the following command on the same directory using terminal/command prompt: ngrok http 1337
Copy the "https" url. (It will be something like https://XXXXXX.ngrok.io)

Step 6: Download account activity dashboard

Git clone https://github.com/twitterdev/account-activity-dashboard.git
run the module using "npm start" using the terminal/command prompt

Step 7: Attach Webhook

open "localhost:5000" on the browser.
Click on "Manage Webhook"
Paste the "ngrok url" into "Create or Update Webhook" field & click submit

Step 8: Add a user/page subscription

Open terminal/Command prompt
Goto "account activity dashboard" folder
execute "node example_scripts/subscription_management/add-subscription-app-owner.js -e "
note: Add user subscription for the user that owns the app.