mirror of
https://github.com/xiamx/awesome-sentiment-analysis.git
synced 2024-10-01 11:48:57 -04:00
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.
This commit is contained in:
Jeffrey Flynt
parent
ecf7196db0
commit
34fdb1aa71
58
README.md
58
README.md
@ -8,22 +8,23 @@ Curated list of Sentiment Analysis methods, implementations and misc.
|
||||
|
||||
<!-- TOC -->
|
||||
|
||||
- [Contents](#contents)
|
||||
- [Objective](#objective)
|
||||
- [Introduction](#introduction)
|
||||
- [Survey Papers](#survey-papers)
|
||||
- [Baseline Systems](#baseline-systems)
|
||||
- [Resources and Corpora](#resources-and-corpora)
|
||||
- [Open Source Implementations](#open-source-implementations)
|
||||
- [NodeJS](#nodejs)
|
||||
- [Java](#java)
|
||||
- [Python](#python)
|
||||
- [R](#r)
|
||||
- [Golang](#golang)
|
||||
- [Ruby](#ruby)
|
||||
- [CSharp](#csharp)
|
||||
- [SaaS APIs](#saas-apis)
|
||||
- [Contributing](#contributing)
|
||||
* [Contents](#contents)
|
||||
* [Objective](#objective)
|
||||
* [Introduction](#introduction)
|
||||
* [Survey Papers](#survey-papers)
|
||||
* [Baseline Systems](#baseline-systems)
|
||||
* [Resources and Corpora](#resources-and-corpora)
|
||||
* [Open Source Implementations](#open-source-implementations)
|
||||
* [NodeJS](#nodejs)
|
||||
* [Java](#java)
|
||||
* [Python](#python)
|
||||
* [R](#r)
|
||||
* [Golang](#golang)
|
||||
* [Ruby](#ruby)
|
||||
* [CSharp](#csharp)
|
||||
* [SaaS APIs](#saas-apis)
|
||||
* [Web Apps](#web-apps)
|
||||
* [Contributing](#contributing)
|
||||
|
||||
<!-- /TOC -->
|
||||
|
||||
@ -34,9 +35,10 @@ The goal of this repository is to provide adequate links for scholars who want t
|
||||
## Introduction
|
||||
|
||||
Sentiment Analysis happens at various levels:
|
||||
- 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.
|
||||
- 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)
|
||||
|
||||
* 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.
|
||||
* 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.
|
||||
|
||||
@ -77,11 +79,13 @@ 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)._
|
||||
|
||||
### 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.
|
||||
|
||||
### Java
|
||||
|
||||
[LingPipe](http://alias-i.com/): Lexical, Corpus-based, Supervised Machine Learning
|
||||
|
||||
[CoreNLP](https://github.com/stanfordnlp/CoreNLP): Supervised Machine Learning, Deep Learning
|
||||
@ -89,6 +93,7 @@ _**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)
|
||||
|
||||
### 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)
|
||||
|
||||
[vivekn/sentiment](https://github.com/vivekn/sentiment): Supervised Machine Learning, Naive Bayes Classifier. [[paper]](https://arxiv.org/abs/1305.6143)
|
||||
@ -109,19 +114,22 @@ 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).
|
||||
|
||||
### R
|
||||
|
||||
[timjurka/sentiment](https://github.com/timjurka/sentiment): Supervised Machine Learning, Naive Bayes Classifier.
|
||||
|
||||
### Golang
|
||||
|
||||
[cdipaolo/sentiment](https://github.com/cdipaolo/sentiment): Supervised Machine Learning, Naive Bayes Classifier. Based on [cdipaolo/goml](https://github.com/cdipaolo/goml).
|
||||
|
||||
### Ruby
|
||||
|
||||
[malavbhavsar/sentimentalizer](https://github.com/malavbhavsar/sentimentalizer): Lexical, Dictionary-based.
|
||||
|
||||
[7compass/sentimental](https://github.com/7compass/sentimental): Lexical, Dictionary-based.
|
||||
|
||||
### 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
|
||||
|
||||
@ -132,11 +140,15 @@ position in the social network to aide sentiment analysis. [[paper]](https://arx
|
||||
* Indico [[web]](https://www.indico.io/)
|
||||
* Rosette API [[web]](https://developer.rosette.com/)
|
||||
|
||||
## Web Apps
|
||||
|
||||
* Textalytic [[web]](https://www.textalytic.com)
|
||||
|
||||
## Contributing
|
||||
|
||||
:+1::tada: First off, thanks for taking the time to contribute! :tada::+1:
|
||||
|
||||
Steps to contribute:
|
||||
|
||||
- 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.
|
||||
* 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.
|
||||
|
||||
Loading…
Reference in New Issue
Block a user