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rutlima sinaga's Projects

brandis icon brandis

Brandis: End-to-end encryption for everyone

document-classification icon document-classification

1. Text pre-processing and exploration: tokenization, lower case, stop words removal, stemming. 2. Document classification: Two feature extraction methods - Bag of words, TF-IDF. Two machine learning models to train a classification model - SVM, Naïve Bayes.

enkrip icon enkrip

membuat chat aplikasi sederhana

extracting-stock-sentiments-from-news-headlines-using-sentimental-analysis icon extracting-stock-sentiments-from-news-headlines-using-sentimental-analysis

In this project, I generated investing insights by applying sentiment analysis on financial news headlines from Finviz. Using this natural language processing technique, I was able to understand the emotion behind the headlines and predict whether the market feels good or bad about a stock. The datasets used in this project are raw HTML files for Facebook (FB) and Tesla (TSLA) stocks from FINVIZ.com, a popular website dedicated to stock information and news. Made a plot to visualize the positive, negative, and neutral scores for a single day of trading and single stock.

fasttext icon fasttext

Library for fast text representation and classification.

flask_user icon flask_user

Mencoba Implementasi Flask User untuk authentikasi dan manajemen hak akses

hands-on-natural-language-processing-with-python icon hands-on-natural-language-processing-with-python

This repository is for my students of Udemy. You can find all lecture codes along with mentioned files for reading in here. So, feel free to clone it and if you have any problem just raise a question.

scrapingdata icon scrapingdata

Code for getting data from social media for your data science projects!

sentiment-analysis-python-with-support-vector-machine icon sentiment-analysis-python-with-support-vector-machine

Sentiment analysis is the process of understanding, extracting and processing data textual automatically to get the sentiment information contained in a sentence of opinion expressed in the form and group them into two groups: positive opinions and negative opinions. The data is used from previous project with lexicon, based on positive and negative sentiments. Several processes in this project are divided data into training and test data with ratio 90:10, calculated the word weight with Term Frequency-Inverse Document Frequency (TF-IDF), use Support Vector Machine on training data to produce the classification model then tested on test data.

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