Article.EikonAPI.Python.NewsSentimentAnalysis | simple sentiment analysis of news | Predictive Analytics library

by Refinitiv-API-Samples Jupyter Notebook Version: Current License: No License

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kandi X-RAY | Article.EikonAPI.Python.NewsSentimentAnalysis Summary

Article.EikonAPI.Python.NewsSentimentAnalysis is a Jupyter Notebook library typically used in Analytics, Predictive Analytics applications. Article.EikonAPI.Python.NewsSentimentAnalysis has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

This article will demonstrate how we can conduct a simple sentiment analysis of news delivered via our new Eikon Data APIs. Natural Language Processing (NLP) is a big area of interest for those looking to gain insight and new sources of value from the vast quantities of unstructured data out there. The area is quite complex and there are many resources online that can help you familiarise yourself with this very interesting area. There are also many different packages that can help you as well as many different approaches to this problem. Whilst these are beyond the scope of this article - I will go through a simple implementation which will give you a swift enough introduction and practical codebase for further exploration and learning. Thomson Reuters Eikon with access to new Eikon Data APIs. Required Python Packages: eikon, pandas, numpy, beautifulsoup, textblob, datetime. Required corpora download: >>>python -m textblob.download_corpora (this is required by the sentiment engine to generate sentiment).

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Article.EikonAPI.Python.NewsSentimentAnalysis has a low active ecosystem.

It has 16 star(s) with 12 fork(s). There are 11 watchers for this library.

It had no major release in the last 6 months.

Article.EikonAPI.Python.NewsSentimentAnalysis has no issues reported. There are no pull requests.

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Article.EikonAPI.Python.NewsSentimentAnalysis has no bugs reported.

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Article.EikonAPI.Python.NewsSentimentAnalysis has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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Community Discussions

Trending Discussions on Predictive Analytics

will TensorFlow utilize GPU for predictive Analysis?

Restructuring Pandas Dataframe for large number of columns

Display data from two json files in react native

QUESTION

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.

...

ANSWER

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.

Source https://stackoverflow.com/questions/64948197

QUESTION

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.

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ANSWER

Answered 2020-Nov-01 at 19:39

You might want try pivoting your table:

Source https://stackoverflow.com/questions/64630691

QUESTION

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

...

ANSWER

Answered 2020-May-17 at 23:55

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

Source https://stackoverflow.com/questions/61859411

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