Sentment Analysis

A curated list of Sentiment Analysis methods, implementations and misc.

Sentiment Analysis is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written languages. (Liu 2012)

Survey Papers

Liu, Bing. "Sentiment analysis and opinion mining." Synthesis lectures on human language technologies 5.1 (2012): 1-167. [pdf]

Vinodhini, G., and R. M. Chandrasekaran. "Sentiment analysis and opinion mining: a survey." International Journal 2.6 (2012): 282-292. [pdf]

Medhat, Walaa, Ahmed Hassan, and Hoda Korashy. "Sentiment analysis algorithms and applications: A survey." Ain Shams Engineering Journal 5.4 (2014): 1093-1113. [pdf]

Open Source Implementations

NodeJS

thisandagain/sentiment: Lexical, Dictionary-based, AFINN-based. thinkroth/Sentimental Lexical, Dictionary-based, AFINN-based.

Java

LingPipe: Lexical, Corpus-based.

CoreNLP: Supervised Machine Learning

Python

nltk: VADER sentiment analysis tool, Lexical, Dictionary-based, Rule-based. [paper]

vivekn/sentiment: Supervised Machine Learning, Naive Bayes Classifier. [paper]

xiaohan2012/twitter-sent-dnn: Supervised Machine Learning, Deep Learning, Convolutional Neural Network. [paper]

kevincobain2000/sentiment_classifier: Supervised Machine Learning, Naive Bayes Classifier, Max Entropy Classifier, SentiWordNet.

pedrobalage/SemevalAspectBasedSentimentAnalysis: Aspect-Based, Supervised Machine Learning, Conditional Random Field.

ganeshjawahar/mem_absa: Aspect-Based, Supervised Machine Learning, Deep Learning, Attention-based, External Memory. [paper]

R

timjurka/sentiment: Supervised Machine Learning, Naive Bayes Classifier.

Golang

cdipaolo/sentiment: Supervised Machine Learning, Naive Bayes Classifier. Based on cdipaolo/goml.

Ruby

malavbhavsar/sentimentalizer: Lexical, Dictionary-based.

7compass/sentimental: Lexical, Dictionary-based.