Awesome-Mirrors/awesome-sentiment-analysis
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@ -8,23 +8,22 @@ Curated list of Sentiment Analysis methods, implementations and misc.
<!-- TOC --> <!-- TOC -->
* [Contents](#contents) - [Contents](#contents)
* [Objective](#objective) - [Objective](#objective)
* [Introduction](#introduction) - [Introduction](#introduction)
* [Survey Papers](#survey-papers) - [Survey Papers](#survey-papers)
* [Baseline Systems](#baseline-systems) - [Baseline Systems](#baseline-systems)
* [Resources and Corpora](#resources-and-corpora) - [Resources and Corpora](#resources-and-corpora)
* [Open Source Implementations](#open-source-implementations) - [Open Source Implementations](#open-source-implementations)
* [NodeJS](#nodejs) - [NodeJS](#nodejs)
* [Java](#java) - [Java](#java)
* [Python](#python) - [Python](#python)
* [R](#r) - [R](#r)
* [Golang](#golang) - [Golang](#golang)
* [Ruby](#ruby) - [Ruby](#ruby)
* [CSharp](#csharp) - [CSharp](#csharp)
* [SaaS APIs](#saas-apis) - [SaaS APIs](#saas-apis)
* [Web Apps](#web-apps) - [Contributing](#contributing)
* [Contributing](#contributing)
<!-- /TOC --> <!-- /TOC -->
@ -35,10 +34,9 @@ The goal of this repository is to provide adequate links for scholars who want t
## Introduction ## Introduction
Sentiment Analysis happens at various levels: Sentiment Analysis happens at various levels:
- Document-level Sentiment Analysis evaluate sentiment of a single entity (i.e. a product) from a review document.
* Document-level Sentiment Analysis evaluate sentiment of a single entity (i.e. a product) from a review document. - Sentence-level Sentiment Analysis evaluate sentiment from a single sentence.
* Sentence-level Sentiment Analysis evaluate sentiment from a single sentence. - 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)
* 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)
Most recent research focuses on the aspect-based approaches. But not all opensource implementations are caught up yet. Most recent research focuses on the aspect-based approaches. But not all opensource implementations are caught up yet.
@ -79,13 +77,11 @@ The characteristics of each implementation are described.
_**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)._ _**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)._
### NodeJS ### NodeJS
[thisandagain/sentiment]( https://github.com/thisandagain/sentiment): Lexical, Dictionary-based, AFINN-based.
[thisandagain/sentiment](https://github.com/thisandagain/sentiment): Lexical, Dictionary-based, AFINN-based.
[thinkroth/Sentimental](https://github.com/thinkroth/Sentimental) Lexical, Dictionary-based, AFINN-based. [thinkroth/Sentimental](https://github.com/thinkroth/Sentimental) Lexical, Dictionary-based, AFINN-based.
### Java ### Java
[LingPipe](http://alias-i.com/): Lexical, Corpus-based, Supervised Machine Learning [LingPipe](http://alias-i.com/): Lexical, Corpus-based, Supervised Machine Learning
[CoreNLP](https://github.com/stanfordnlp/CoreNLP): Supervised Machine Learning, Deep Learning [CoreNLP](https://github.com/stanfordnlp/CoreNLP): Supervised Machine Learning, Deep Learning
@ -93,7 +89,6 @@ _**Caveats**: A key problem in sentiment analysis is its sensitivity to the doma
[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) [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)
### Python ### Python
[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) [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)
[vivekn/sentiment](https://github.com/vivekn/sentiment): Supervised Machine Learning, Naive Bayes Classifier. [[paper]](https://arxiv.org/abs/1305.6143) [vivekn/sentiment](https://github.com/vivekn/sentiment): Supervised Machine Learning, Naive Bayes Classifier. [[paper]](https://arxiv.org/abs/1305.6143)
@ -114,23 +109,20 @@ position in the social network to aide sentiment analysis. [[paper]](https://arx
[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). [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).
### R ### R
[timjurka/sentiment](https://github.com/timjurka/sentiment): Supervised Machine Learning, Naive Bayes Classifier. [timjurka/sentiment](https://github.com/timjurka/sentiment): Supervised Machine Learning, Naive Bayes Classifier.
### Golang ### Golang
[cdipaolo/sentiment](https://github.com/cdipaolo/sentiment): Supervised Machine Learning, Naive Bayes Classifier. Based on [cdipaolo/goml](https://github.com/cdipaolo/goml). [cdipaolo/sentiment](https://github.com/cdipaolo/sentiment): Supervised Machine Learning, Naive Bayes Classifier. Based on [cdipaolo/goml](https://github.com/cdipaolo/goml).
### Ruby ### Ruby
[malavbhavsar/sentimentalizer](https://github.com/malavbhavsar/sentimentalizer): Lexical, Dictionary-based. [malavbhavsar/sentimentalizer](https://github.com/malavbhavsar/sentimentalizer): Lexical, Dictionary-based.
[7compass/sentimental](https://github.com/7compass/sentimental): Lexical, Dictionary-based. [7compass/sentimental](https://github.com/7compass/sentimental): Lexical, Dictionary-based.
### CSharp ### CSharp
[amrish7/Dragon](https://github.com/amrish7/Dragon): Supervised Machine Learning, Naive Bayes Classifier. [amrish7/Dragon](https://github.com/amrish7/Dragon): Supervised Machine Learning, Naive Bayes Classifier.
## SaaS APIs ## SaaS APIs
* Google Cloud Natural Language API [[web]](https://cloud.google.com/natural-language/) * Google Cloud Natural Language API [[web]](https://cloud.google.com/natural-language/)
@ -140,15 +132,11 @@ position in the social network to aide sentiment analysis. [[paper]](https://arx
* Indico [[web]](https://www.indico.io/) * Indico [[web]](https://www.indico.io/)
* Rosette API [[web]](https://developer.rosette.com/) * Rosette API [[web]](https://developer.rosette.com/)
## Web Apps
* Textalytic [[web]](https://www.textalytic.com)
## Contributing ## Contributing
:+1::tada: First off, thanks for taking the time to contribute! :tada::+1: :+1::tada: First off, thanks for taking the time to contribute! :tada::+1:
Steps to contribute: Steps to contribute:
* Make your awesome changes - Make your awesome changes
* Submit pull request; if you add a new entry, please give a very brief explanation why you think it should be added. - Submit pull request; if you add a new entry, please give a very brief explanation why you think it should be added.