StormTweetsSentimentAnalysis | Computes sentiment analysis of tweets of US States | Predictive Analytics library

 by   P7h Java Version: Current License: Apache-2.0

kandi X-RAY | StormTweetsSentimentAnalysis Summary

kandi X-RAY | StormTweetsSentimentAnalysis Summary

StormTweetsSentimentAnalysis is a Java library typically used in Analytics, Predictive Analytics applications. StormTweetsSentimentAnalysis has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

This repository contains an application which is built to demonstrate as an example of Apache Storm distributed framework by performing sentiment analysis of tweets originating from U.S. in real-time. This Topology retrieves tweets originating from US and computes the sentiment scores of States [based on tweets] continuously i.e. till the time the topology is killed. User has to explicitly kill the topology for exiting the application. Apache Storm is an open source distributed real-time computation system, developed at BackType by Nathan Marz and team. It has been open sourced by Twitter [post BackType acquisition] in August, 2011. And Storm became a top level project in Apache on 29th September, 2014. This application has been developed and tested initially with Storm v0.8.2 on Windows 7 in local mode; and was eventually updated and tested with Storm v0.9.3 on 01st January, 2015. Application may or may not work with earlier or later versions than Storm v0.9.3.

            kandi-support Support

              StormTweetsSentimentAnalysis has a low active ecosystem.
              It has 62 star(s) with 28 fork(s). There are 9 watchers for this library.
              It had no major release in the last 6 months.
              StormTweetsSentimentAnalysis has no issues reported. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of StormTweetsSentimentAnalysis is current.

            kandi-Quality Quality

              StormTweetsSentimentAnalysis has no bugs reported.

            kandi-Security Security

              StormTweetsSentimentAnalysis has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              StormTweetsSentimentAnalysis is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              StormTweetsSentimentAnalysis releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed StormTweetsSentimentAnalysis and discovered the below as its top functions. This is intended to give you an instant insight into StormTweetsSentimentAnalysis implemented functionality, and help decide if they suit your requirements.
            • Processes the given tweet
            • Reverse the status of the given Latitude and longitude
            • Get the state of a Tweet from a GeoLocation object
            • Get the state of a Tweet
            • Main entry point of tweets
            • Logs the score of each sentences
            • Get the sentiment of a tweet
            • Declares the output fields
            • Cleans up resources
            • Compares two sentiment values
            • Polls the next tuple
            • Main entry point
            • Open the spout
            • Demonstrates how to use the Google Maps API
            • Initialize the state
            • Removes all the URLs from a tweet
            • Reads the afiniment file into a map
            • Declare the output fields
            Get all kandi verified functions for this library.

            StormTweetsSentimentAnalysis Key Features

            No Key Features are available at this moment for StormTweetsSentimentAnalysis.

            StormTweetsSentimentAnalysis Examples and Code Snippets

            No Code Snippets are available at this moment for StormTweetsSentimentAnalysis.

            Community Discussions


            will TensorFlow utilize GPU for predictive Analysis?
            Asked 2020-Nov-21 at 21:35

            GPU is good for parallel computing but the problem is some machine learning libraries don't utilize the GPU, unless that machine learning based on image processing or some sort of graphics processing, what if I am using machine learning for predictive Analytics? do libraries like TensorFlow utilize the GPU? or they use only CPU? or can I choose which processing unit to use? whats the deal here?

            note: predictive Analysis requires no graphics processing.



            Answered 2020-Nov-21 at 21:35
            The short answer: yes, it will! The slightly longer answer:

            The computation that happens in the GPU in any of the machine learning frameworks that support GPUs is not limited to graphical processing. For instance, if your model is a simple logistic regression, a framework such as TensorFlow will run it on the GPU if properly configured.

            The advantage of GPUs for machine learning is that training big neural networks benefits greatly from the high level of parallelism that the GPUs offer.

            If you want to know more about this, I'd recommend you start here or here.

            some things to consider:
            • how much a model will benefit from running in the GPU will depend on how much it will benefit from parallel computation in general.
            • Deep Learning models can be applied to predictive analytics, as well as more classical machine learning models. Bear in mind that neural nets are possibly the category of models that will benefit inherently from the GPU (see links above).
            • Even though running models using GPUs (or even more specialised hardware) can bring benefits, I would suggest that you don't choose a framework and, especially, don't choose an algorithm based solely on the fact that it will benefit from parallelism, but rather look at how appropriate a given algorithm is for the data you have.



            Restructuring Pandas Dataframe for large number of columns
            Asked 2020-Nov-01 at 19:39

            I have a pandas dataframe which is a large number of answers given by users in response to a survey and I need to re-structure it. There are up to 105 questions asked each year, but I only need maybe 20 of them.

            The current structure is as below.

            What I want to do is re-structure it so that the row values become column names and the answer given by the user is then the value in that column. In a picture (from Excel), what I want is the below (I know I'll need to re-name my columns, but that's fine once I can create the structure in the first place):

            Is it possible to re-structure my dataframe this way? The outcome of this is to use some predictive analytics to predict a target variable, so I need to re-strcture before I can use Random Forest, kNN, and so on.



            Answered 2020-Nov-01 at 19:39

            You might want try pivoting your table:



            Display data from two json files in react native
            Asked 2020-May-17 at 23:55

            I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files



            Answered 2020-May-17 at 23:55

            The new object to get params in React Navigation 5 is:


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install StormTweetsSentimentAnalysis

            You can download it from GitHub.
            You can use StormTweetsSentimentAnalysis like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the StormTweetsSentimentAnalysis component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer For Gradle installation, please refer .


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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