Saravanan666/pypi-org huggingface dataset:
Package: sentiment-analysis
Sentiment_analysis (감성 분석)일기 및 일상 평문 텍스트에서, 글쓴이의 감정을 유추하기 위해서 만들어진 라이브러리입니다. 감성 분석을 위해서,
Keras 및 nltk가 사용되었습니다. 또한, 텍스트의 길이에 따라서 문장을 요약하고 이에 대한 감성을 각각 분석을 하기 위해
Lexrank 알고리즘이 사용되었습니다.해당 라이브러리는 감성을 다음 6가지의 종류로 분류합니다. (우울함, 분노, 불안, 고통, 당황, 행복)※ 주의)이 라이브러리는 한국어와 영어를 제외한 다른 언어를 지원하지 않습니다. 추후, 일어, 중어 및 다른 언어들에 대한 지원을 할 예정입니다.This library is made for analyzing sentiment from a single sentence or paragraph. It uses Keras and nltk.
In addition, It also uses LexRank algorithm to summarize a paragraph.It classifies sentiment as 6 emotions (Depression, Anger, Anxiety, Agony, Embarrassed, Happiness)※ Caution)This library does not support other languages except English and Korean yet. However, we have a plan to support other languages includes Japanese, Chinese, and etc.Getting agony score won't work, if detected language is English. Be careful.Installationpip install sentiment-analysisUsagefromsentimentimportSentimentAnalysissentiment_analysis=SentimentAnalysis(want_to_analyze_sentence_or_paragraph)sentiment_analysis.analyze()# It initializes analysis progresssadness_score=sentiment_analysis.get_sadness_score()# get depression score.anger_score=sentiment_analysis.get_anger_score()# get anger score.anxiety_score=sentiment_analysis.get_anxiety_score()# get anxiety score.agony_score=sentiment_analysis.get_agony_score()# get agony score.embarrassed_score=sentiment_analysis.get_embarrassed_score()# get embarrassed score.happiness_score=sentiment_analysis.get_happiness_score()# get happiness score.total_score=sentiment_analysis.get_total_score()# get all scores as list type.positive_score=sentiment_analysis.get_positive_score()# get positive scoresnegative_score=sentiment_analysis.get_negative_score()# get negative scoresReference)https://github.com/theeluwin/lexrankr(Korean Lexrank)
Package: sentiment-analysis-csci-e89
sentiment_analysys_csci_e89This package was designed to enable its users to perform end to end sentiment analysis with state of the art techniques.The api assumes a common data model that is described in great detail in the
documentation. In short, the modules expect tabular datasets with the following fields for training data:text_idtextlabeland the following fields for live test data:text_idtextThe api contains 5 main modules:data_cleaning: A class that was written to support a number of popular machine learning datasets. It cleans the raw data and
structures it in a way that the other modules can use.pre_processing : A class that provides a number of high level functions to perform sophisticated data transformations and cleaning.    This class is responsible for preparing the raw text data for our Neural architectures.modeling : A class that provides a number of methods, each dedicated to training a certain type of architecture. Refer to the documentation for the exact specification of each of the architectures provided.pretrained_embeddings : A class that provides methods to prepare well known and popular word embeddings (GloVe and word2vec) in a format that our netoworks can work with. We require that the user download the raw data from the appropriate sources. Once again, details are included in the documentation.predict_newdata: A class that provides methods to use our trained networks to make predictions on live data. Live data as I define it here can be thought of test data that is processed and prepared outside of the original efforts that processed the data our model was trained and validated against.A number of different neural architectures are provided with easy to call methods, thereby allowing you to train sophisticated models with no more than a few lines of code.Some of the architectures implement transfer learning and require that certain files be downloaded
locally.Please refer to the documentation and the tutorial script.The turorial is in the form of a jupyter notebook with a step by step implementation. Please find it here:https://github.com/stefano10p/-sentiment_analysis_csci_e89-/tree/master/tutorialInstallationRun the following to install:pipinstallsentiment-analysis-csci-e89Please download the documentation from here:https://github.com/stefano10p/-sentiment_analysis_csci_e89-/tree/master/docs/_build/htmlCreate a local directory on your machine with each of the html files.You may also download this package from my github:https://github.com/stefano10p/-sentiment_analysis_csci_e89-You will find a requirements.txt file when you clone the repository.
On your machine create a virtual environemnt:conda create --name sentiment_analysisActivate the environment and use the requirements file to configure it with all the necessary dependencies.conda activate sentiment_analysispip install -r requirements.txtYou are ready to use the package.
Thank you !
Package: sentiment-analysis-spanish
sentiment-spanishHow does it work?sentiment-spanish is a python library that uses convolutional neural networks to predict the sentiment of spanish sentences. The model was trained using over800000reviews of users of the pageseltenedor, decathlon, tripadvisor, filmaffinity and ebay.
This reviews were extracted using web scraping with the projectopinion-reviews-scraperUsing the rate in the user reviews we trained the model to learn from the language in them. For that we use the libraries Keras and Tensorflow. We achieved a validation accuracy (accuracy over fresh data, no used for training) of 88%.
For more details regarding the training of the neural network model check the reposentiment-analysis-model-neural-networkWhy?I believe there are not many solutions to sentiment analysisin spanishbased on neural networks.Install and useFirst to install the packagepip install sentiment-analysis-spanishYou will also need keras and tensorflowpip install keras tensorflowImport the packagefrom sentiment_analysis_spanish import sentiment_analysisrun the sentiment analysis:sentiment = sentiment_analysis.SentimentAnalysisSpanish()
print(sentiment.sentiment("me gusta la tombola es genial"))you will see that it outputs1For instance if you use the textsentiment = sentiment_analysis.SentimentAnalysisSpanish()
print(sentiment.sentiment("me parece terrible esto que me estás diciendo"))you will see that it outputs9.460418e-14swhich as you see is very close to 0.Output and meaningThe functionsentiment(text)returns a number between 0 and 1. This is the probability of string variabletextof being "positive". Low probabilities mean that the text is negative (numbers close to 0), high probabilities (numbers close to 1) mean that the text is positive. The space in between corespond to neutral texts.
Package: sentimentanalyzer
[]SentimentAnalyzerThis Library is Developed using WebScrapping Techiques.It Takes a String as input and output is JSON DataThis is developed using BS4 and requests and re ModulesInstallationpipinstallsentimentanalyzerUsageimportsysfromsentimentanalyzer.sentimentanalyzerimportSentimenttext="""Hello this is a  sample  textplace your text here  and it should be less than 50 000 Words"""obj=Sentiment(text=text)data=obj.getprint(data)AuthorsSoumil Nitin ShahLicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details
Package: sentiment_classifier
Sentiment Classification using WSDpip install sentiment_classifierHomeDownloadGithubTry OnlineOverviewSentiment Classifier using Word Sense Disambiguation usingwordnetand word occurance
statistics from movie review corpusnltk. Classifies into positive and negative categories.Online DemoTry OnlineRequirementsInVersion 0.5all the following requirements are installed automatically. In case of troubles install those manually.You must have Python 2.6.NLTKhttp://www.nltk.org2.0 installed.NumPyhttp://numpy.scipy.orgSentiWordNethttp://sentiwordnet.isti.cnr.itHow to InstallShell commandpython setup.py installDocumentationhttp://pythonhosted.org/sentiment_classifier/Script UsageShell Commands:senti_classifier -c file/with/review.txtPython UsageShell Commandscd sentiment_classifier/src/senti_classifier/
python senti_classifier.py -c reviews.txtLibrary Usagefrom senti_classifier import senti_classifier
sentences = ['The movie was the worst movie', 'It was the worst acting by the actors']
pos_score, neg_score = senti_classifier.polarity_scores(sentences)
print pos_score, neg_score
... 0.0 1.75from senti_classifier.senti_classifier import synsets_scores
print synsets_scores['peaceful.a.01']['pos']

... 0.25History0.6Bug Fixed upon nltk upgrade0.5No additional data required trained data is loaded automatically. Much faster/Optimized than previous versions.0.4Added Bag of Words as a Feature as occurance statistics0.3Sentiment Classifier First app, Using WSD module
Package: sentiment-investor
SentiPyWelcome to the Sentiment Investor API wrapper. This package can be used to easily access trending stocks and individual ticker data from the sentimentinvestor.com website.InstallingSentiPy can be installed withpip:$ python3 -m pip install sentiment-investorOn macOS, it can also be installed viaMacPorts:$ sudo port install py-sentipySetupTo use this package you will need a developer token and key, which can be obtained fromsentimentinvestor.com/developer/dashboard.DocumentationSentiPy has usage and reference documentation atsentimentinvestor.com/developer/python-docs.🤝 ContributingContributions, issues and feature requests are welcome!Feel free to check theissues page.Geting StartedFirst, fork theGitHub projectto your account. Then, run the following with your GitHub handle in place ofINSERT_GITHUB_NAME:$gitclonehttps://github.com/INSERT_GITHUB_NAME/sentipy
$cdsentipy
$poetryinstall&&poetryshell
$pre-commitinstallThis then runs the formatters when you're about to commit any changes.AuthorsThe following people have made notable contributions to the development of this library (in chronological order):Rob StockTimothy LangerHaren SThe full list of contributors ishere.LicenceSentiPy is made available under the MIT Licence. For more details, seeLICENSE.txt.
Package: sentimentizer
IntroductionBeta release, api subject to change. Install with:pip install sentimentizerThis repo contains Neural Nets written with the pytorch framework for sentiment analysis.A LSTM based torch model can be found in the rnn folder. In spite of large language models (GPT3.5 as of 2023)
dominating the conversation, small models can be pretty effective and are nice to learn from. This model focuses on sentiment analysis and was trained on
a single gpu in minutes and requires less than 1GB of memory.Usage# where 0 is very negative and 1 is very positive
from sentimentizer.tokenizer import get_trained_tokenizer
from sentimentizer.rnn.model import get_trained_model

model = get_trained_model(64, 'cpu')
tokenizer = get_trained_tokenizer()
review_text = "greatest pie ever, best in town!"
positive_ids = tokenizer.tokenize_text(review_text)
model.predict(positive_ids)
  
>> tensor(0.9701)Install for development with miniconda:conda create -n {env}  
conda install pip  
pip install -e .Retrain modelTo rerun the model:get the yelpdataset,get the glove 6B 100Ddatasetplace both files in the package data directoryrun the training script in workflows
Package: sentiment-lexicon
Sentiment lexicon wrapper and generator.Installationpip install sentiment-lexiconUsageThe module provides a single class,Lexicon, that can be used as a simple wrapper around sentiment lexicon data.
The sentiment value of a given word can be accessed via thevalueinstance method.fromsentiment_lexiconimportLexiconlexicon=Lexicon(words,values)lexicon.value('good')# => 1The class can also generate a sentiment lexicon based on positive and negative input documents.lexicon=Lexicon.from_labelled_text(positive_documents,negative_documents)More information is available in thedocumentation.
Package: sentimentpl
sentimentPLPyTorch models for Polish language sentiment regression based on allegro/herbert and CLARIN-PL datasetInstallationsentimentPL is available on PyPI, so You can just run:$ pip3 install sentimentplBasic UsageFor a given sentence, the model produces output value from (-1;1) range (from most negative to most positive).fromsentimentpl.modelsimportSentimentPLModelmodel=SentimentPLModel(from_pretrained='latest')print(model('Jestem wesoły Romek').item())Note:The model uses transformers API to load pretrained embedding models from their repository.
They should be downloaded and cached on Your machine.Note:The model loads pretrained state dicts for final regression layers from a file included in the package files
(as its size does not exceed 1MB). This will be changed in the future, so the model would be loaded from
external repository.TrainingFor training You would probably want to download the source code by cloning the repository:$ git clone https://github.com/philvec/sentimentPL.gitDownload training data fromhttps://clarin-pl.eu/dspace/bitstream/handle/11321/710/dataset_conll.zipand unzip it tosentimentpl/data.In the main repository dir, run$ python3 ./sentimentpl/train.pyVersion historyv.0.0.6 latestmodel better trained to MSE ~0.307, added HerBERT finetuning optionv.0.0.5Basic 3-layer MLP with ReLU and input Dropout.References:Kocoń, Jan; Zaśko-Zielińska, Monika and Miłkowski, Piotr, 2019, PolEmo 2.0 Sentiment Analysis Dataset for CoNLL, CLARIN-PL digital repository,http://hdl.handle.net/11321/710.T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi,P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer,P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. L. Scao,S. Gugger, M. Drame, Q. Lhoest, and A. M. Rush, “Transformers:State-of-the-art natural language processing,” inProceedings of the2020 Conference on Empirical Methods in Natural LanguageProcessing: System Demonstrations, (Online), pp. 38–45, Associationfor Computational Linguistics, Oct. 2020.P. Rybak, R. Mroczkowski, J. Tracz, and I. Gawlik, “Klej:Comprehensive benchmark for polish language understanding,” 2020
Package: sentimeter
SentimeterThe module is published in pypi (https://pypi.org/project/sentimeter/) . you can install it viapip install sentimeterIntroductionSentimeter is all about identifiying the sentiments from a source . The source can be a text / audio .
Below are some of the built in modules shipping along with the package .Audio ModuleProcess the Emotions in a given audio file. Currently it supports only wav file . In future we will add more formatsLive SpeechProcess the Emotions in a live speechTelegram ModeSentimeter has a Telegram Bot, it can process the emotions in a Telegram chatText FileProcess a text file and identifies the emotionsYoutube VideoUnder DevelopmentIncase if you would like to create own modules, you can do it by extending the BaseSource class  like belowclass OwnSentimeterSource(BaseSource):
    def __init__(self, engine) -> None:
        super().__init__(engine)
        pass

    def run(self):
        # The text is is the input to the engine
        self.engine.process(text)
        return True

    def on_event(self, results):
        # This overridden function will get you the results(map of emotions)
        print(results)We can use multiple backends as well with this engine. I have created an experimental ChatGPT backend . But by default it uses a built in model to identify the emotions.Class DiagramPackage DiagramUsageThere are applications developed using sentimeter. Please refer apps folder inhttps://github.com/karthagokul/sentimeterTodoDocumentationGoogle Hangout ConnectorSetupCreate a Virtual envpy -m venv virtual-env
.\virtual-env\Scripts\activateInstall All Dependenciespip3 install .
Package: sentimizer
SentimizerAboutSentimizer will measure sentiment around specific entities within text.  It is built on NLTK, Spacy, and NRCLex.  Output is a dictionary that can be analyzed further, graphed, formulated into a wordcloud, etc.Referenceshttps://github.com/explosion/spaCyhttps://github.com/nltkhttps://github.com/metalcorebear/NRCLexRevision History2022-10-17: initial commit.Example Usagepip install sentimizerInstantiate SentiMizer Objectfrom sentimizer import SentiMizeranalyzer = SentiMizer()Load TextLoads initial body of text.analyzer.load_text(text : str)attributes:analyzer.text- attribute contains loaded text (str).Append TextFor appending additional text to the initial input.analyzer.append_text(text : str)Entity RecognitionFor identifying entities within the loaded body of text.analyzer.find_entities()optional parameters:entity_types_of_interest- list of entity types for recognition.  Default value is['ORG', 'PERSON', 'FAC', 'GPE', 'LOC', 'EVENT']All possible lables include:CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ARTFor a description of each, visithttps://spacy.io/models/en.attributes:analyzer.entities- dictionary of entities and their tags (dict).analyzer.sentences- dictionary of entities and concatenated sentences containing each entity (dict).  Keys are entities and values are the concatenated sentences mentioning that entity.Measure Emotional ContentFor measuring sentiment and emotional affect of sentences that mention each entity.analyzer.emote()attributes:analyzer.sentiments- Vader composite sentiment scores for each entity (dict).  Keys are entities and values are the composite sentiment score for that entity.analyzer.affect- NRCLex affect scores for each entity (dict).  Keys are entities and the values are affect frequency dictionaries.
optional parameters:entity_type- string specifying the entity type to analyze. Default value isNone.  Default action is the analyze all entity types.
Package: sentinel1-reader
s1-readerA package to read Sentinel-1 data into the ISCE3-compatible burst class.FeaturesCreate ISCE3-compatible Sentinel1 burst class given:S1 SAFEsubswath indexpolarizationpath to orbit directoryMonotonically increasing bursts IDs.🚨 This toolbox is still inpre-alphastage and undergoingrapid development. 🚨InstallDownload source code:gitclonehttps://github.com/opera-adt/s1-reader.gitInstall dependencies:condainstall-cconda-forge--files1-reader/requirements.txtInstalls1-readervia pip:# run "pip install -e" to install in development modepython-mpipinstall./s1-readerUsageThe following sample code demonstrates how to process a single burst from a S1 SAFE zip:imports1readerzip_path="S1A_IW_SLC__1SDV_20190909T134419_20190909T134446_028945_03483B_B9E1.zip"swath_num=2pol="VV"# read orbitsorbit_dir='/home/user/data/sentinel1_orbits'orbit_path=s1reader.get_orbit_file_from_dir(zip_path,orbit_dir,auto_download=True)# returns the list of the burstsbursts=s1reader.load_bursts(zip_path,orbit_path,swath_num,pol)burst_ids=[x.burst_idforxinbursts]LicenseCopyright (c) 2021California Institute of Technology (“Caltech”). U.S. Government sponsorship acknowledged.All rights reserved.Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.Neither the name of Caltech nor its operating division, the Jet Propulsion Laboratory, nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Package: sentinel2
sentinel2A Python package for processing Sentinel-2 Level-1C productFree software: GNU General Public License v3Documentation:https://sentinel2.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.1 (2020-06-25)First release on PyPI.
Package: sentinel2-downloader
Overviewsentinel2_downloaderis a Python library that allows you to search, download, and retrieve metadata of Sentinel satellite images from the Copernicus Data Space.FeaturesSearch for Sentinel satellite images based on various parameters such as footprint, date range, product type, cloud cover percentage, and platform name.Download Sentinel satellite images using their product IDs.Save downloaded images to the specified directory.Display a progress bar during the download process, showing the download speed, elapsed time, and percentage of completion.InstallationYou can installsentinel2_downloaderusing pip:pipinstallsentinel2_downloaderUsageHere’s an example of how to usesentinel2_downloader:fromsentinel2_downloaderimportSentinelAPI# Create an instance of the SentinelAPIapi=SentinelAPI(username='your_username',password='your_password')# Perform a search for Sentinel satellite imagesproducts=api.query(footprint='POLYGON((longitude1 latitude1, longitude2 latitude2, longitude3 latitude3, ...))',start_date='2020-01-01',end_date='2020-01-31',cloud_cover_percentage='20',product_type='MSIL1C',platform_name='SENTINEL-2')iflen(products)>1:forproductinproducts:# Download the imagesa=api.download(product_id=product['Id'],directory_path='output')Args:footprint (str): The footprint to define the geographic area of interest. It should be in the Well-Known Text (WKT) format.start_date (str): The start date of the data acquisition period in the format ‘yyyy-mm-dd’.end_date (str): The end date of the data acquisition period in the format ‘yyyy-mm-dd’.product_type (str): The type of Sentinel product to query.cloud_cover_percentage (str): The maximum cloud cover percentage allowed.platform_name (str): The name of the Sentinel platform.
Package: sentinel5dl
The sentinel5dl project consists of a library and a command line tool which provide easy access toemission data productsoriginating from the European Space Agency’s Sentinel-5P satellite.sentinel5dl on PyPIDocumentationIssue trackerInstallationInstall this library using:%> pip install sentinel5dlQuick Example (Library)fromsentinel5dlimportsearch,download# Search for Sentinel-5 productsresult=search(polygon='POLYGON((7.8 49.3,13.4 49.3,13.4 52.8,7.8 52.8,7.8 49.3))',begin_ts='2019-09-01T00:00:00.000Z',end_ts='2019-09-17T23:59:59.999Z',product='L2__CO____',processing_level='L2',processing_mode='Offline')# Download found products to the local folderdownload(result.get('products'))Quick Example (Binary)Download carbon monoxide sensor data taken between 2019-01-08 and 2019-01-20 to
the directory/datausing eight workers (eight parallel downloads):sentinel5dl--worker8\--begin-ts2019-01-08\--end-ts2019-01-20\/dataTo show all available options, run:sentinel5dl-h
Package: sentinel-datetime
Sentinel DatetimeFor python-dateutilInspired bya stackoverflow question,
for when you want to know a little more about the
datetime strings you are parsing with python-dateutilInstallpip install sentinel-datetimeUsage>>>fromdateutilimportparser>>>>>>fromsentinel_datetimeimportsentinel>>>>>>date=parser.parse("Sep-2nd 11:00:00",default=sentinel())>>>date.has_dayTrue>>>date.has_yearFalse>>>date.todatetime()2021-09-0211:00:00