Sigit Purnomo

Sigit Purnomo

Senior Lecturer at Informatics Department, Universitas Atma Jaya Yogyakarta

Collections of Github Repository in Python for Sentiment Analysis Task

1 minute read

Sentiment Analysis ( SA) is a field of study that analyzes people’s feelings or opinions from reviews or opinions Pandey & Deorankar, 2019. Sentiment analysis can be seen as a natural language processing task, the task is to develop a system that understands people’s language. I have tried to collect and curate some Python-based Github repository linked to the sentiment analysis task, and the results were listed here. Please enjoy it to support your research about sentiment analysis using Python!

Last updated: May 25, 2020
Source : Github

No. Year Title Github
1 2019 CCF-BDCI-Sentiment-Analysis-Baseline - The code for CCF-BDCI-Sentiment-Analysis-Baseline. View
2 2018 fsauor2018 - Code for Fine-grained Sentiment Analysis of User Reviews of AI Challenger 2018. View
3 2018 senticomment - Gets the sentiment of YouTube comments. View
4 2018 booksoup - Booksoup allows you to analyse and traverse your downloaded facebook data, including features such as sentiment analysis and message frequency analysis over time. View
5 2017 sentiment-discovery - Unsupervised Language Modeling at scale for robust sentiment classification. View
6 2017 vaderSentiment - VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner). View
7 2017 generating-reviews-discovering-sentiment - “Learning to Generate Reviews and Discovering Sentiment”. View
8 2016 Sentiment-Analysis-Twitters. View
9 2016 NSC - Neural Sentiment Classification View
10 2016 hotel-review-analysis - Sentiment analysis and aspect classification for hotel reviews us. View
11 2015 sentimentAPI - A fast python scikit-learn text sentiment API server. View
12 2015 twitter-sent-dnn - Deep Neural Network for Sentiment Analysis on Twitter. View
13 2014 TextBlob - Simple, Pythonic, text processing–Sentiment analysis, part-of-speech tagging, noun phrase extraction. View

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