sentiment | Sentiment Analysis using logistic regression | Predictive Analytics library

by srom Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | sentiment Summary

sentiment is a Python library typically used in Analytics, Predictive Analytics applications. sentiment has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However sentiment build file is not available. You can download it from GitHub.

Sentiment Analysis using logistic regression (via gradient descent).

Support

Quality

Security

License

Reuse

Support

sentiment has a low active ecosystem.

It has 15 star(s) with 3 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

sentiment has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of sentiment is current.

Quality

sentiment has 0 bugs and 0 code smells.

Security

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

sentiment code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

sentiment is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

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

sentiment saves you 80 person hours of effort in developing the same functionality from scratch.

It has 206 lines of code, 13 functions and 2 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Calculate gradient descent .
Train the model .
Compute n - grams from a document .
Get the most common n - grams from the training set .
Calculate the cost function
Batch descent .
Stochastic descent .
Return the sigmoid function

Get all kandi verified functions for this library.

sentiment Key Features

No Key Features are available at this moment for sentiment.

sentiment Examples and Code Snippets

No Code Snippets are available at this moment for sentiment.

Community Discussions

Trending Discussions on sentiment

TorchText Vocab TypeError: Vocab.__init__() got an unexpected keyword argument 'min_freq'

What is the loss function used in Trainer from the Transformers library of Hugging Face?

How to get the text using XPATH

Sum table values per day

how to iteratively SELECT by hour for averages by userid?

Rust compiler not optimising lzcnt? (and similar functions)

Sentiment analysis Python tokenization

Compare two column Pandas row per row

I'm learning webpack and I'm trying to make an api call, but it's not working. Here's my code

Threading Tasks Queue in Flask

QUESTION

TorchText Vocab TypeError: Vocab.__init__() got an unexpected keyword argument 'min_freq'

Asked 2022-Apr-04 at 09:26

I am working on a CNN Sentiment analysis machine learning model which uses the IMDb dataset provided by the Torchtext library. On one of my lines of code

vocab = Vocab(counter, min_freq = 1, specials=('\', '\', '\', '\'))

I am getting a TypeError for the min_freq argument even though I am certain that it is one of the accepted arguments for the function. I am also getting UserWarning Lambda function is not supported for pickle, please use regular python function or functools partial instead. Full code

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ANSWER

Answered 2022-Apr-04 at 09:26

As https://github.com/pytorch/text/issues/1445 mentioned, you should change "Vocab" to "vocab". I think they miss-type the legacy-to-new notebook.

correct code:

Source https://stackoverflow.com/questions/71652903

QUESTION

What is the loss function used in Trainer from the Transformers library of Hugging Face?

Asked 2022-Mar-23 at 10:12

What is the loss function used in Trainer from the Transformers library of Hugging Face?

I am trying to fine tine a BERT model using the Trainer class from the Transformers library of Hugging Face.

In their documentation, they mention that one can specify a customized loss function by overriding the compute_loss method in the class. However, if I do not do the method override and use the Trainer to fine tine a BERT model directly for sentiment classification, what is the default loss function being use? Is it the categorical crossentropy? Thanks!

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ANSWER

Answered 2022-Mar-23 at 10:12

It depends! Especially given your relatively vague setup description, it is not clear what loss will be used. But to start from the beginning, let's first check how the default compute_loss() function in the Trainer class looks like.

You can find the corresponding function here, if you want to have a look for yourself (current version at time of writing is 4.17). The actual loss that will be returned with default parameters is taken from the model's output values:

loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]

which means that the model itself is (by default) responsible for computing some sort of loss and returning it in outputs.

Following this, we can then look into the actual model definitions for BERT (source: here, and in particular check out the model that will be used in your Sentiment Analysis task (I assume a BertForSequenceClassification model.

The code relevant for defining a loss function looks like this:

Source https://stackoverflow.com/questions/71581197

QUESTION

How to get the text using XPATH

Asked 2022-Feb-16 at 13:26

Bullish

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ANSWER

Answered 2022-Feb-16 at 13:26

You don't need the "text()" in the xpath:

Source https://stackoverflow.com/questions/71142430

QUESTION

Sum table values per day

Asked 2022-Feb-01 at 19:25

I have a table as shown in the image, where each comment has a publication date, with year, month, day and time, I would like to add the sentiment values by day.

this is how the table is composed

serie <- data.frame(comments$created_time,sentiment2$positive-sentiment2$negative)

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ANSWER

Answered 2022-Feb-01 at 18:58

Using dplyr you can do:

Source https://stackoverflow.com/questions/70945176

QUESTION

how to iteratively SELECT by hour for averages by userid?

Asked 2022-Jan-11 at 14:03

I'm trying to average some metrics by user id, and by hour, in a select statement.

Would love some help understanding the best approach to doing this without using parameters in a python script :P

this is the current query,

...

ANSWER

Answered 2022-Jan-11 at 13:47

In posgresql you can group by year, month, day and hours using,

Source https://stackoverflow.com/questions/70667395

QUESTION

Rust compiler not optimising lzcnt? (and similar functions)

Asked 2021-Dec-26 at 01:56

What was done:

This follows as a result of experimenting on Compiler Explorer as to ascertain the compiler's (rustc's) behaviour when it comes to the log2()/leading_zeros() and similar functions. I came across this result with seems exceedingly both bizarre and concerning:

Compiler Explorer link

Code:

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ANSWER

Answered 2021-Dec-26 at 01:56

Old x86-64 CPUs don't support lzcnt, so rustc/llvm won't emit it by default. (They would execute it as bsr but the behavior is not identical.)

Use -C target-feature=+lzcnt to enable it. Try.

More generally, you may wish to use -C target-cpu=XXX to enable all the features of a specific CPU model. Use rustc --print target-cpus for a list.

In particular, -C target-cpu=native will generate code for the CPU that rustc itself is running on, e.g. if you will run the code on the same machine where you are compiling it.

Source https://stackoverflow.com/questions/70481312

QUESTION

Sentiment analysis Python tokenization

Asked 2021-Dec-10 at 14:34

my problem is the follow: I want to do a sentiment analysis on Italian tweet and I would to tokenise and lemmatise my Italian text in order to find new analysis dimension for my thesis. The problem is that I would like to tokenise my hashtag, splitting also the composed one. For example if I have #nogreenpass, I would have also without the # symbol, because the sentiment of the phrase would be better understand with all word of the text. How could I do this? I tried with sapCy, but I have no results. I created a function to clean my text, but I can't have the hashtag in the way I would. I'm using this code:

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ANSWER

Answered 2021-Dec-10 at 14:34

You can tweak your current clean_code with

Source https://stackoverflow.com/questions/70304914

QUESTION

Compare two column Pandas row per row

Asked 2021-Nov-29 at 13:47

I want to compare 2 column. If same will True if not same will False like this:

filtering lemmatization check [hello, world] [hello, world] True [grape, durian] [apple, grape] False

The output from my code is all False. But, the data actually is different. Why?

You can get my data github

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ANSWER

Answered 2021-Nov-29 at 13:47

Here is difference between columns - in one column missing '' around strings, possible solution is convert both columns to lists, for comapre use Series.eq (working like ==):

Source https://stackoverflow.com/questions/70155570

QUESTION

I'm learning webpack and I'm trying to make an api call, but it's not working. Here's my code

Asked 2021-Nov-03 at 05:42

This is the server

...

ANSWER

Answered 2021-Nov-03 at 05:42

Try this.

Source https://stackoverflow.com/questions/69817045

QUESTION

Threading Tasks Queue in Flask

Asked 2021-Oct-31 at 21:24

Hey guys so I'm building a websocket using flask and I'm currently creating a new thread for every request so that thread can do some heavy processing and not take too much time to return something from the request. The problem is that at some point i get so many threads open that it starts causing problems and i was wondering if i could setup some queue in flask to limit the app for creating like 8 thread each time only.

My code:

...

ANSWER

Answered 2021-Oct-29 at 13:05

This is a pretty common problem when multi-threading, the solution you need here is called the producer-consumer model where there is a producer (the entity that creates work) then there is a (threadsafe) queue where this work is pushed into, then there are consumers (worker threads) that one by one pop out work from the queue until the queue is empty.

Doing this limits the number of worker threads. One neat way to do this is to use the concurrent.futures library available in python. @aaron as given an appropriate link for that.

Source https://stackoverflow.com/questions/69567112

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

Vulnerabilities

No vulnerabilities reported

Install sentiment

You can download it from GitHub.
You can use sentiment like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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