sentiment-analysis | Positive or negative sentiment analysis from text | Predictive Analytics library

by kramamur Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | sentiment-analysis Summary

sentiment-analysis is a Python library typically used in Analytics, Predictive Analytics, Deep Learning, Pytorch, Tensorflow, Keras applications. sentiment-analysis has no bugs, it has no vulnerabilities and it has low support. However sentiment-analysis build file is not available. You can download it from GitHub.

The idea here is to extract a high level "Positive" or "Negative" sentiment from a given piece of text. This can be useful in predicting customer churn for instance by analysing email interactions of ongoing support queries. The project is still an experiment. At the moment, I am using gensim's doc2vec library based on Mikolov and Le's paper[1]. I am training a doc2vec model using a training dataset and then using simple logistic regression to predict a 2-way coarse-grained positive/negative probability. I combined the test and train dataset of phrases from imdb and added cornell's sentiment polarity v2.0 dataset to the mix. The data was already curated from my source, but I removed stopwords using nltk. Still experimenting with different d2v models but at the moment it seems like pv-dbow gives good results for this dataset (dm=0). Negative sampling is turned off (hs=1) and this seems to make some difference. Perhaps removing the stopwords compromises the context I am not sure yet (but pv-dbow should ignore context words already?).

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sentiment-analysis has a low active ecosystem.

It has 2 star(s) with 4 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of sentiment-analysis is current.

Quality

sentiment-analysis has no bugs reported.

Security

sentiment-analysis has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

sentiment-analysis does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

sentiment-analysis releases are not available. You will need to build from source code and install.

sentiment-analysis has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed sentiment-analysis and discovered the below as its top functions. This is intended to give you an instant insight into sentiment-analysis implemented functionality, and help decide if they suit your requirements.

Train the model .
Initialize the model .
Scoring the model using Logistic regression .
Return a dictionary of source files
Transform text into words .
update progress bar

Get all kandi verified functions for this library.

sentiment-analysis Key Features

No Key Features are available at this moment for sentiment-analysis.

sentiment-analysis Examples and Code Snippets

No Code Snippets are available at this moment for sentiment-analysis.

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

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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

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

Vulnerabilities

No vulnerabilities reported

Install sentiment-analysis

Install Anaconda python3 from here if you don't already have it
Fork or clone the repo git clone https://github.com/kramamur/sentiment-analysis.git
Dependencies - conda install gensim nltk numpy scikit-learn (numpy and scikit come with conda bjic)
Download nltk data - nltk.download('punkt') and nltk.download('stopwords')
Train the model - python train.py
Insert your favorite text into infer.txt- Yelp reviews give fairly accurate results
Predict - python sentiment.py

Support

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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