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Add Textalytic
Adding textalytic to a new section called Web App, since the SaaS section specifies API's. Textalytic covers Topic Modeling, Named Entity Recognition, Sentiment Analysis, Corpus Builder, POS Tags, & more in browser.
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Jeffrey Flynt
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README.md
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README.md
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# 😀😄😂😭 Awesome Sentiment Analysis 😥😟😱😤 [](https://github.com/sindresorhus/awesome)
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# 😀😄😂😭 Awesome Sentiment Analysis 😥😟😱😤 [](https://github.com/sindresorhus/awesome)
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Curated list of Sentiment Analysis methods, implementations and misc.
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@ -8,22 +8,23 @@ Curated list of Sentiment Analysis methods, implementations and misc.
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<!-- TOC -->
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- [Contents](#contents)
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- [Objective](#objective)
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- [Introduction](#introduction)
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- [Survey Papers](#survey-papers)
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- [Baseline Systems](#baseline-systems)
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- [Resources and Corpora](#resources-and-corpora)
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- [Open Source Implementations](#open-source-implementations)
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- [NodeJS](#nodejs)
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- [Java](#java)
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- [Python](#python)
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- [R](#r)
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- [Golang](#golang)
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- [Ruby](#ruby)
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- [CSharp](#csharp)
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- [SaaS APIs](#saas-apis)
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- [Contributing](#contributing)
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* [Contents](#contents)
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* [Objective](#objective)
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* [Introduction](#introduction)
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* [Survey Papers](#survey-papers)
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* [Baseline Systems](#baseline-systems)
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* [Resources and Corpora](#resources-and-corpora)
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* [Open Source Implementations](#open-source-implementations)
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* [NodeJS](#nodejs)
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* [Java](#java)
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* [Python](#python)
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* [R](#r)
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* [Golang](#golang)
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* [Ruby](#ruby)
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* [CSharp](#csharp)
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* [SaaS APIs](#saas-apis)
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* [Web Apps](#web-apps)
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* [Contributing](#contributing)
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<!-- /TOC -->
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@ -34,9 +35,10 @@ The goal of this repository is to provide adequate links for scholars who want t
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## Introduction
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Sentiment Analysis happens at various levels:
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- Document-level Sentiment Analysis evaluate sentiment of a single entity (i.e. a product) from a review document.
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- Sentence-level Sentiment Analysis evaluate sentiment from a single sentence.
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- Aspect-level Sentiment Analysis performs finer-grain analysis. For example, the sentence “the iPhone’s call quality is good, but its battery life is short.” evaluates two aspects: call quality and battery life, of iPhone (entity). The sentiment on iPhone’s call quality is positive, but the sentiment on its battery life is negative. (Liu 2012)
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* Document-level Sentiment Analysis evaluate sentiment of a single entity (i.e. a product) from a review document.
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* Sentence-level Sentiment Analysis evaluate sentiment from a single sentence.
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* Aspect-level Sentiment Analysis performs finer-grain analysis. For example, the sentence “the iPhone’s call quality is good, but its battery life is short.” evaluates two aspects: call quality and battery life, of iPhone (entity). The sentiment on iPhone’s call quality is positive, but the sentiment on its battery life is negative. (Liu 2012)
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Most recent research focuses on the aspect-based approaches. But not all opensource implementations are caught up yet.
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_**Caveats**: A key problem in sentiment analysis is its sensitivity to the domain from which either training data is sourced, or on which a sentiment lexicon is built. [[♠]](http://www.springer.com/gp/book/9783319389707) Be careful assuming off-the-shelf implementations will work for your problem, make sure to look at the model assumptions and validate whether they’re accurate on your own domain [[♦]](https://lobste.rs/s/zsfqyk/curated_list_sentiment_analysis_methods/comments/ge671n#c_ge671n)._
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### NodeJS
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[thisandagain/sentiment]( https://github.com/thisandagain/sentiment): Lexical, Dictionary-based, AFINN-based.
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[thisandagain/sentiment](https://github.com/thisandagain/sentiment): Lexical, Dictionary-based, AFINN-based.
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[thinkroth/Sentimental](https://github.com/thinkroth/Sentimental) Lexical, Dictionary-based, AFINN-based.
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### Java
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[LingPipe](http://alias-i.com/): Lexical, Corpus-based, Supervised Machine Learning
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[CoreNLP](https://github.com/stanfordnlp/CoreNLP): Supervised Machine Learning, Deep Learning
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@ -89,6 +93,7 @@ _**Caveats**: A key problem in sentiment analysis is its sensitivity to the doma
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[ASUM](http://uilab.kaist.ac.kr/research/WSDM11/): Unsupervised Machine Learning, Latent Dirichlet Allocation. [[paper]](http://www.cs.cmu.edu/~yohanj/research/papers/WSDM11.pdf)
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### Python
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[nltk](http://www.nltk.org/): [VADER](https://github.com/cjhutto/vaderSentiment) sentiment analysis tool, Lexical, Dictionary-based, Rule-based. [[paper]](http://comp.social.gatech.edu/papers/icwsm14.vader.hutto.pdf)
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[vivekn/sentiment](https://github.com/vivekn/sentiment): Supervised Machine Learning, Naive Bayes Classifier. [[paper]](https://arxiv.org/abs/1305.6143)
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[thunlp/NSC](https://github.com/thunlp/NSC): Deep Learning, Attention-based. Uses user and production information.[[paper]](http://anthology.aclweb.org/D/D16/D16-1171.pdf).
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### R
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[timjurka/sentiment](https://github.com/timjurka/sentiment): Supervised Machine Learning, Naive Bayes Classifier.
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### Golang
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[cdipaolo/sentiment](https://github.com/cdipaolo/sentiment): Supervised Machine Learning, Naive Bayes Classifier. Based on [cdipaolo/goml](https://github.com/cdipaolo/goml).
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### Ruby
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[malavbhavsar/sentimentalizer](https://github.com/malavbhavsar/sentimentalizer): Lexical, Dictionary-based.
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[7compass/sentimental](https://github.com/7compass/sentimental): Lexical, Dictionary-based.
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### CSharp
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[amrish7/Dragon](https://github.com/amrish7/Dragon): Supervised Machine Learning, Naive Bayes Classifier.
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[amrish7/Dragon](https://github.com/amrish7/Dragon): Supervised Machine Learning, Naive Bayes Classifier.
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## SaaS APIs
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@ -132,11 +140,15 @@ position in the social network to aide sentiment analysis. [[paper]](https://arx
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* Indico [[web]](https://www.indico.io/)
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* Rosette API [[web]](https://developer.rosette.com/)
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## Web Apps
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* Textalytic [[web]](https://www.textalytic.com)
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## Contributing
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:+1::tada: First off, thanks for taking the time to contribute! :tada::+1:
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Steps to contribute:
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- Make your awesome changes
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- Submit pull request; if you add a new entry, please give a very brief explanation why you think it should be added.
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* Make your awesome changes
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* Submit pull request; if you add a new entry, please give a very brief explanation why you think it should be added.
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