Augmented Analytics With PySpark and Sentiment Analysis

In this tutorial, you will learn how to enrich COVID19 tweets data with a positive sentiment score.You will leverage PySpark and Cognitive Services and learn about Augmented Analytics.

What Is Augmented Analytics?

According to Gartner’s report, augmented analytics is the use of technologies such as machine learning and AI to assist with data preparation, insight generation. Its main goal is to help more people to get value out of data and generate insights in an easy, conversational manner. For our example, we extract the positive sentiment score out of a tweet to help in understanding the overall sentiment towards COVID-19.

What Is PySpark?

PySpark is the framework we use to work with Apache Spark and Python. Learn more about it here.

What Is Sentiment Analysis?

Sentiment Analysis is part of NLP - natural language processing usage that combined text analytics, computation linguistics, and more to systematically study affective states and subjective information, such as tweets. In our example, we will see how we can extract positive sentiment score out of COVID-19 tweets text. In this tutorial, you are going to leverage Azure Cognitive Service, which gives us Sentiment Analysis capabilities out of the box. When working with it, we can leverage the TextAnalyticsClient client library or leverage REST API. Today, you will use the REST API as it gives us more flexibility.

Prerequisites

  • Apache Spark environment with notebooks, it can be Databricks, or you can start a local environment with docker by running the next command: docker run -it -p 8888:8888 jupyter/pyspark-notebook
  • Azure free account
  • Download Kaggle COVID-19 Tweet data
  • Cognitive Services free account (check out the picture below )

Step by Step Tutorial — Full Data Pipeline:

In this step by step tutorial, you will learn how to load the data with PySpark, create a user define a function to connect to Sentiment Analytics API, add the sentiment data and save everything to the Parquet format files.

You now need to extract upload the data to your Apache Spark environment, rather it’s Databricks or PySpark jupyter notebook. For Databricks use this, for juypter use this.

For both cases, you will need the file_location = "/FileStore/tables/covid19_tweets.csv" make sure to keep a note of it.

#python #augmented analytics #pyspark #sentiment-analysis

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Augmented Analytics With PySpark and Sentiment Analysis

Augmented Analytics With PySpark and Sentiment Analysis

In this tutorial, you will learn how to enrich COVID19 tweets data with a positive sentiment score.You will leverage PySpark and Cognitive Services and learn about Augmented Analytics.

What Is Augmented Analytics?

According to Gartner’s report, augmented analytics is the use of technologies such as machine learning and AI to assist with data preparation, insight generation. Its main goal is to help more people to get value out of data and generate insights in an easy, conversational manner. For our example, we extract the positive sentiment score out of a tweet to help in understanding the overall sentiment towards COVID-19.

What Is PySpark?

PySpark is the framework we use to work with Apache Spark and Python. Learn more about it here.

What Is Sentiment Analysis?

Sentiment Analysis is part of NLP - natural language processing usage that combined text analytics, computation linguistics, and more to systematically study affective states and subjective information, such as tweets. In our example, we will see how we can extract positive sentiment score out of COVID-19 tweets text. In this tutorial, you are going to leverage Azure Cognitive Service, which gives us Sentiment Analysis capabilities out of the box. When working with it, we can leverage the TextAnalyticsClient client library or leverage REST API. Today, you will use the REST API as it gives us more flexibility.

Prerequisites

  • Apache Spark environment with notebooks, it can be Databricks, or you can start a local environment with docker by running the next command: docker run -it -p 8888:8888 jupyter/pyspark-notebook
  • Azure free account
  • Download Kaggle COVID-19 Tweet data
  • Cognitive Services free account (check out the picture below )

Step by Step Tutorial — Full Data Pipeline:

In this step by step tutorial, you will learn how to load the data with PySpark, create a user define a function to connect to Sentiment Analytics API, add the sentiment data and save everything to the Parquet format files.

You now need to extract upload the data to your Apache Spark environment, rather it’s Databricks or PySpark jupyter notebook. For Databricks use this, for juypter use this.

For both cases, you will need the file_location = "/FileStore/tables/covid19_tweets.csv" make sure to keep a note of it.

#python #augmented analytics #pyspark #sentiment-analysis

Sofia  Maggio

Sofia Maggio

1626077565

Sentiment Analysis in Python using Machine Learning

Sentiment analysis or opinion mining is a simple task of understanding the emotions of the writer of a particular text. What was the intent of the writer when writing a certain thing?

We use various natural language processing (NLP) and text analysis tools to figure out what could be subjective information. We need to identify, extract and quantify such details from the text for easier classification and working with the data.

But why do we need sentiment analysis?

Sentiment analysis serves as a fundamental aspect of dealing with customers on online portals and websites for the companies. They do this all the time to classify a comment as a query, complaint, suggestion, opinion, or just love for a product. This way they can easily sort through the comments or questions and prioritize what they need to handle first and even order them in a way that looks better. Companies sometimes even try to delete content that has a negative sentiment attached to it.

It is an easy way to understand and analyze public reception and perception of different ideas and concepts, or a newly launched product, maybe an event or a government policy.

Emotion understanding and sentiment analysis play a huge role in collaborative filtering based recommendation systems. Grouping together people who have similar reactions to a certain product and showing them related products. Like recommending movies to people by grouping them with others that have similar perceptions for a certain show or movie.

Lastly, they are also used for spam filtering and removing unwanted content.

How does sentiment analysis work?

NLP or natural language processing is the basic concept on which sentiment analysis is built upon. Natural language processing is a superclass of sentiment analysis that deals with understanding all kinds of things from a piece of text.

NLP is the branch of AI dealing with texts, giving machines the ability to understand and derive from the text. For tasks such as virtual assistant, query solving, creating and maintaining human-like conversations, summarizing texts, spam detection, sentiment analysis, etc. it includes everything from counting the number of words to a machine writing a story, indistinguishable from human texts.

Sentiment analysis can be classified into various categories based on various criteria. Depending upon the scope it can be classified into document-level sentiment analysis, sentence level sentiment analysis, and sub sentence level or phrase level sentiment analysis.

Also, a very common classification is based on what needs to be done with the data or the reason for sentiment analysis. Examples of which are

  • Simple classification of text into positive, negative or neutral. It may also advance into fine grained answers like very positive or moderately positive.
  • Aspect-based sentiment analysis- where we figure out the sentiment along with a specific aspect it is related to. Like identifying sentiments regarding various aspects or parts of a car in user reviews, identifying what feature or aspect was appreciated or disliked.
  • The sentiment along with an action associated with it. Like mails written to customer support. Understanding if it is a query or complaint or suggestion etc

Based on what needs to be done and what kind of data we need to work with there are two major methods of tackling this problem.

  • Matching rules based sentiment analysis: There is a predefined list of words for each type of sentiment needed and then the text or document is matched with the lists. The algorithm then determines which type of words or which sentiment is more prevalent in it.
  • This type of rule based sentiment analysis is easy to implement, but lacks flexibility and does not account for context.
  • Automatic sentiment analysis: They are mostly based on supervised machine learning algorithms and are actually very useful in understanding complicated texts. Algorithms in this category include support vector machine, linear regression, rnn, and its types. This is what we are gonna explore and learn more about.

In this machine learning project, we will use recurrent neural network for sentiment analysis in python.

#machine learning tutorials #machine learning project #machine learning sentiment analysis #python sentiment analysis #sentiment analysis

How are Companies using Analytics for their customers?

This article is about applications of analytics used by Leading Firms.

These Firms are using:-

  1. Better Data
  2. Better Exploratory Methods
  3. Better Predictive Methods
  4. Better Optimization to make better business decisions that influence the products and services they sell

So let me start with the first one.

1. KOHL’S Department Store

Image for post

Kohl’s Department Store is a very large national chain department store. They’re doing what’s called Smartphone Targeting. So for example, they have data on your geospatial location which they get when you walk in their store & turn on your Wi-Fi in your cell phone.

Everyone’s cell phone has a fixed IP address, so they can know it’s you. They can now link that what you have browsed before if you’ve gone on to their website kohls.com.

Let’s say they know that Mr. X was standing in front of the shoe aisle. They can now actually send him a real time discount for shoes, whether through text or or any promo code. They know physically where there customer is standing.

And now, the action they are taking is to send him a targeted or contextual discount given his physical location.

This is extremely valuable data. It’s not just selling the person the right product, it’s the right product at the right place at the right time.

Kohl’s is taking advantage,_ It’s not valuable 30 minutes before that person get to the store, it’s not as valuable 30 minutes after, it’s valuable right when he’s standing there_. They can use that data for decision making, and they’re going to ope-rationalize against it.

Here’s another one,

2. NETFLIX

Image for post

So let me tell you what Netflix is doing. Netflix is doing what’s called meta tagging data meaning of course, when you log onto Netflix, they know what you watch. This is the ultimate in customer analytics.

They can measure customer by customer what it is they’re watching. But here’s what they’re also doing. Every show you watch gets what’s called meta tags like attributes or descriptors.

So they know if Mr. X watched a police show that takes place in the 1970s in a warm weather city. So imagine having that corpus of data from millions and millions of customers.

Well now, rather than saying, what show could we create?

Now, imagine the director sitting there saying, I see what the data’s telling me. People really like police shows that take place in warm weather cities in the 1970s. Hey, let’s create a police show from warm weather in the 1970s.

And so what companies like Netflix are doing is they’re using data mining and customer analytic methods to create content.

#predictive-analytics #analytics #regression-analysis #data-science #customer-analytics #data analysis

sophia tondon

sophia tondon

1620898918

Unlocking Secrets Of Staff Augmentation | Perks For Enterprises & Startup

Startups face a great scarcity of tech talent to establish their companies. That’s why they have to look out for offshore vendors and remote developers. Actually, to carry out rigorous coding tasks, startups have to look for experts in particular technologies who not just code but also manage specific tasks. Hence, it enhances the overall capabilities of their in-house team, hiring developers is getting popular.

Moreover, it also enables companies to control their project resources, management process, milestone accomplishment, and deliveries.

Surely, you, too, would be looking for developers to complete your project in time. So, I have good news for startups that outsourcing isn’t the only available option out there to get their project developed. You can use the IT staff augmentation model to achieve their goal for less time and money.

Read Full Blog - https://medium.com/predict/unlocking-secrets-of-staff-augmentation-perks-for-enterprises-startup-4e7291ce4775

##staff augmentation companies #staff augmentation company #staff augmentation services #it staff augmentation companies #it staff augmentation #staff augmentation services india

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