I am working on sentiment analysis and have implemented the same using multiple libraries like: VaderSentiment, Flair, TextBlob...etc
I now want to implement NRC Lexicon based. The closest ones i could find were:
a) NRCLex Pypi package (this is highly noisy)
b) https://github.com/raffg/harry_potter_nlp/blob/master/sentiment_analysis.ipynb
Any leads with respect to NRC Lexicon implementation using python for Sentiment Analysis is much appreciated.
Suresh Kumar Mukhiya
I used R. It seems you are looking for Python packages. Here are R packages list. May be their documentation site can give you some pointers about Python.
- TidyText: See this guideand this introduction to TidyText. (https://www.tidytextmining.com/sentiment.html)
- R package Lexicon: See docimentation here. (https://cran.rstudio.com/web/packages/lexicon/lexicon.pdf)
- Syuzhet: For literary analysis. (https://cran.r-project.org/web/packages/syuzhet/vignettes/syuzhet-vignette.html)
- The AffectiveTweets Package: Felipe Bravo-Marquez implemented AffectiveTweets for the Weka machine learning workbench that provides a collection of filters for extracting features from tweets for sentiment classification/regression and other related tasks. The package is especially useful to generate feature vectors from a large number of affect lexicons. The vector can then be concatenated to other features vectors (say dense-distributed representations of the text) to improve perfomance. (You can use the feature vector with any classifier -- not just one with support from Weka.)
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