vaderSentiment | VADER ( Valence Aware Dictionary | Natural Language Processing library

 by   cjhutto Python Version: 3.3.2 License: MIT

kandi X-RAY | vaderSentiment Summary

kandi X-RAY | vaderSentiment Summary

vaderSentiment is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. vaderSentiment has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install vaderSentiment' or download it from GitHub, PyPI.

VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.

            kandi-support Support

              vaderSentiment has a medium active ecosystem.
              It has 3977 star(s) with 960 fork(s). There are 142 watchers for this library.
              It had no major release in the last 12 months.
              There are 39 open issues and 77 have been closed. On average issues are closed in 216 days. There are 9 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of vaderSentiment is 3.3.2

            kandi-Quality Quality

              vaderSentiment has 0 bugs and 0 code smells.

            kandi-Security Security

              vaderSentiment has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              vaderSentiment code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              vaderSentiment is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              vaderSentiment releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              vaderSentiment saves you 118 person hours of effort in developing the same functionality from scratch.
              It has 297 lines of code, 7 functions and 3 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed vaderSentiment and discovered the below as its top functions. This is intended to give you an instant insight into vaderSentiment implemented functionality, and help decide if they suit your requirements.
            • Return the sentiment score of the given text
            • Calculate the sentiment of a list of sentiment scores
            • Add accentuation
            • Simplify text
            • Checks if the given value is a valid value
            • Normalize a score
            • Calculate the sentiment valence of a given item
            • Simplify a text
            • Remove punctuation
            • Calculate the sum of a word
            • Determine if input is negated
            • Calculate the value of the negation check
            • Check specialidioms
            • Compute the sentiment score of a given sentiment
            • Read a file
            • Read lines from a file
            • Append line data to a file
            • Remove whitespace from text
            • Pad a reference
            Get all kandi verified functions for this library.

            vaderSentiment Key Features

            No Key Features are available at this moment for vaderSentiment.

            vaderSentiment Examples and Code Snippets

            Section 3 a. Pre-requisites
            Pythondot img1Lines of Code : 33dot img1no licencesLicense : No License
            copy iconCopy
            1. pip install kafka
            2. pip install pandas
            3. pip install json
            4. pip install textblob
            5. pip install nltk
            6. pip install gensim
            7. pip install spacy
            8. pip install re
            9. pip install psycopg2
            10. pip install django
            11. pip install Ipython
            12. pip ins  
            Godot img2Lines of Code : 13dot img2License : Permissive (MIT)
            copy iconCopy
            import (
            analyzer := govader.NewSentimentIntensityAnalyzer()
            sentiment := analyzer.PolarityScores("Usage is similar to all the other ports.")
            fmt.Println("Compound score:", sentiment.Compound)

            Community Discussions


            Using Python (Selenium) to Scrape IMDB (.click() is not working)
            Asked 2021-Jul-28 at 14:04

            I am trying to scrape a list of specific movies from IMDB using this tutorial.

            The code is working fine expect for the for click to get the URL then saves in content. It is not working. The issue is that nothing change in chrome when running the code I really appreciate if anyone can help.



            Answered 2021-Jul-28 at 14:04

            You are using a wrong locator.
            To open the a search result on Google page you should use this:



            How can I automate the form filling process for a user on my webpage with voice via their microphone?
            Asked 2021-Jun-21 at 08:16

            I have a webpage with a web form with flask. Currently, users will need to manually enter their information into the webpage. Then it's appended to a table that they are redirected to once clicking submit. The setup is basically: video is autoplayed and asks user questions, the user fills out their answers manually, once clicking submit, they see their answers are appended to a table.

            I want to reduce the clutter of the page and make it so that the user can verbally give their responses to the video questions. I've read about getusermedia, websockets, and WebRTCs, but am getting confused about them. I've looked all over here, youtube, reddit, and the like. Specifically, here, here, here, and here without much luck.

            I'm thinking a simply for loop with speech recognizer with the different variable in a dict and then passing the data as is, but i'm not sure how to connect that microphone action with the frontend in particular. Isn't the front end where all of the data resides, so we need an http request to obtain it and analyze it? Here's my code:




            Answered 2021-Jun-21 at 05:46

            Actually you cannot use flask for speech recognition. Flask is a backend framework and runs on the server you host it on. Since you want that the speech said by the user should be recognized, you need to use something that is on the client side, i.e, using JavaScript. You could use this tutorial to complete your task.



            pipenv - Pipfile.lock is not being generated due to the 'Could not find a version that matches' error
            Asked 2021-Jun-03 at 06:29

            As the title clearly describes the issue I've been experiencing, no Pipfile.lock is being generated as I get the following error when I execute the recommended command pipenv lock --clear:



            Answered 2021-Jun-03 at 06:29

            By looking at the pypi site for keras-nightly library, I could see that there are no versions named Check which package is generating the error and try downgrading that package.



            How do I make/convert my python app into an Rshiny app? Its a brainteaser! Unable to find what change UI needs in R
            Asked 2021-Apr-01 at 02:59

            i am new to R and trying to understand Rshiny to build UIs. I am trying to create a UI for my python app that transcribes mulitple wav files. There are two parts below, first my python app and the second my shiny app in R which uses reticulate to call my app. For some reason though, i do not receive any output.

            My Python app works perfectly and does NOT need code review.However, the Rshiny app does not execute the python app correctly to produce the desired result. The objective is to let the user transcribe the files from the UI and decide if they want to download the csv.

            I have a python app for transcribing files called



            Answered 2021-Apr-01 at 02:59

            In shiny, you need to pass argument properly in python script. An easy way around is to define a function in a python script and call that function in shiny.

            Here is your modified python script (edited process_data function and added run_script function) -



            How can I bulk/batch transcribe wav files using python?
            Asked 2021-Mar-06 at 06:17

            im trying to use my python app to transcribe multiple files in a folder and speed up the process. At present I am able to do it one file at a time -



            Answered 2021-Mar-03 at 16:48

            have you tried running this script multiple times? you could write a wrapper that launches this script in a subprocess kinda like this:



            VaderSentiment: unable to update emoji sentiment score
            Asked 2020-Aug-04 at 14:39

            As title states, code is as follows:



            Answered 2020-Aug-04 at 14:39

            So apparently Vader transforms emojis to their word representation prior to extracting sentiment. You can find this mapping in "site-packages/vaderSentiment/emoji_utf8_lexicon.txt".

            Updating the code to:



            How do i convert output from a vader sentiment script into a dataframe for a csv
            Asked 2020-Apr-15 at 22:47

            im looking to convert my output to a dataframe format for sentiment scores

            I have an output dataframe:



            Answered 2020-Apr-15 at 22:47
            from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
            analyzer = SentimentIntensityAnalyzer()
            text = ['She is pretty', 'He is ugly']
             scores = []
            for txt in text:
                vs = analyzer.polarity_scores(txt)
            data = pd.DataFrame(text, columns= ['Text'])
            data2 = pd.DataFrame(scores)
            final_dataset= pd.concat([data,data2], axis=1)


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


            No vulnerabilities reported

            Install vaderSentiment

            You can install using 'pip install vaderSentiment' or download it from GitHub, PyPI.
            You can use vaderSentiment 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.


            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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