inimikocok's Stars
explosion/spaCy
💫 Industrial-strength Natural Language Processing (NLP) in Python
vprusso/youtube_tutorials
Collection of scripts corresponding to LucidProgramming YouTube tutorials
sastrawi/sastrawi
[Inactive] High quality stemmer library for Indonesian Language (Bahasa)
har07/PySastrawi
Indonesian stemmer. Python port of PHP Sastrawi project.
agalea91/twitter_search
High level script for finding tweets using Python 3 and Tweepy
ravikiranj/twitter-sentiment-analyzer
Twitter Sentiment Analyzer
masdevid/ID-Stopwords
Stopwords collection of Bahasa Indonesia collected from many sources.
rhnvrm/labeled-tweet-generator
Search for tweets and download the data labeled with its polarity in CSV format
llSourcell/twitter_sentiment_challenge
Twitter Sentiment Analysis Challenge for Learn Python for Data Science #2 by @Sirajology on Youtube
keyreply/Bahasa-Indo-NLP-Dataset
riochr17/Analisis-Sentimen-ID
Analisis Sentimen Twitter dengan TFIDF-ANN
gitlaura/get_tweets
Python script to download tweets for any Twitter user using Tweepy and the Twitter API
ramaprakoso/analisis-sentimen
proyek akhir
yasirutomo/python-sentianalysis-id
Sentiment analysis bahasa Indonesia Python
ridife/dataset-idsa
Indonesia Sentiment Analysis Dataset
masdevid/sentistrength_id
Sentiment Strength Detection in Bahasa Indonesia
kevalmorabia97/pyTweetCleaner
Python module to clean twitter JSON data or tweet text and remove unnecessary data such as hyperlinks, comments on someone else's tweet, non-ASCII chars, non-English tweets, and much more
ahtuz/sentiment-analysis-indonesia
Sentiment Analysis Twitter Bahasa Indonesia dengan TextBlob. Diaplikasikan untuk menganalisis tentang topik debat capres-cawapres 2019
KTakatsuji/Twitter-Sentiment-Naive-Bayes
Twitter Sentiment Analysis using Naive Bayes
satyanugraha/classifying-twitter-user-as-resident-or-tourist
Researches confirms that social media provides good insights on what people think, feel, concern, etc. It is expected that those insight mined from Twitter data has potential to support a better decision-making, especially in public sectors. Public sector wants to know local’s insight level; therefore they need to make sure they use the conversation from residents. However, the ground truth shows that tweets are mixed from the residents and tourist. This study investigates the best automatic fashion model to classify tweets posted by resident and tourist, in NTB. Indonesia. To do so, several consecutive phases were conducted. Those are pre-processing, data training, classification system, data testing, accuracy comparison, and result visualization. First of all, a Twitter dataset, which has 700,000 tweets posted by approximately 26,000 users in Nusa Tenggara Barat, Indonesia was prepared. The dataset divided into two sets, tweets from 4,000 users for data training and 22,000 users for data testing. Then, three popular classification algorithms were applied to the datasets. There are Multinomial Naïve Bayes, Support Vector Machines and Decision Tree. After that, 7 features are created. There are Bag of Words, Normalizer location, Total Tweet, Total Day, Tweet per Day, Total Location and Location per Day. Experiment shows that Multinomial Naïve Bayes with Bag of Words feature has 86% accuracy, while the rest of features give less than 65% accuracy. This is different with Support Vector Machines and Decision Tree results. These two algorithms produce better accuracy results excluding Bag of Words feature. It implies that Support Vector Machine and Decision Tree are more powerful when processing numerical value. However, among all classification system, Multinomial Naïve Bayes still being the most accurate algorithm for the model.
bintangbuntoro/SemevalSentimentAnalysis
Twitter sentiment analysist for Semeval 2016 dataset task 4 using lexicon based and support vector machine.
ishanray/twitter-scraper
Non-API Twitter Scraper
khaerulumam42/scrap_currency
Tutorial web scrapping currency data
jineshdhruv8/US-Airlines-Twitter-Sentiment
Three machine learning techniques are used – support vector machine, Naïve Bayes and neural networks. The accuracy of support vector machine, Naïve Bayes and neural network on validation data-set are 82%, 72% and 55% respectively.
ArunSharma92/Influence-Detection-and-Analysis
Collection of User Specific Data using TWITTER REST API. Establishing Ground Truth for Data: Using 3 Human Annotators Training And Prediction of Classifier: Using the Human Annotated Data for Training the Classifier using the Support Vector Machine Algorithm and then prediction. (WEKA, Lightside). Language used: Python Tools: Tweepy Library, Stanford CoreNLP 3.6.0 Suite, Twitter Rest API, WEKA, Lightside.
christopher-j-snyder/Twitter-data-mining
Retrieve tweets from Twitter via Rest API and assign them a class label using a Support Vector Machine.
murugappan91/Twitter_Analysis_Using_Classifiers
Project to analyze tweets based on 2012 US Presidential election to predicted percentage of candidate supporters using SVM (Support Vector Machine) and Naïve Bayesian Classifier in Python.
pankaj512/sarcasm-on-twitter
This project is about creating a classifier to categories tweet as sarcastic or not sarcastic with the help of machine learning by using to algorithm Naive-Bayes and Support Vector machine.
puputirfansyah/tweepy
pujoseno-Crawling-Twitter-Hashtag
yasirutomo/tweet-scrapper
Pengambilan data Twitter (scrapping) dengan Python