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{
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   "source": [
    "### 1 High Dimensional Model"
   ]
  },
  {

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{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 High Dimensional Model"
   ]
  },
  {

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code is for ZestFinance modeling team interview homework assisgnment. ML algorithms including Regularized Logistic Regression, Elastic Net, Random Fores and Gradient Boosting (xgboost) are applied."
   ]
  },
  {

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import urllib2
from bs4 import BeautifulSoup as BeautifulSoup

def get_list(ticker):

    base_url_part1 = "http://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK="
    base_url_part2 = "&type=&dateb=&owner=&start="
    base_url_part3 = "&count=100&output=xml"
    href = []
    

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Sentiment Analysis using Naive Bayes
====================================
* Naive Bayes
* Add-1 smoothing
* 10-fold cross validation
* regular expression detecting negation words

Besides the regular method, the code also realized: 
* Boolean Naive Bayes
* Naive Bayes with stop word

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Sentiment Analysis using sklearn
=================================
* sklearn LinearSVC
* 10-fold cross validation
* accuracy 88.45%

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Sentiment analysis experiment using scikit-learn
================================================

The script sentiment.py reproduces the sentiment analysis approach from Pang,
Lee and Vaithyanathan (2002), who tried to classify movie reviews as positive
or negative, with three differences:

* tf-idf weighting is applied to terms
* the three-fold cross validation split is different
* regularization is tuned by cross validation

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import nltk
from nltk.corpus import wordnet as wn

### Synsets and lemmas
# For an arbitrary word, i.e. dog, it may have different senses, and we can find its synsets.
wn.synsets('dog')

# Once you have a synset, there are functions to find the information on that synset, 
# and we will start with “lemma_names”, “lemmas”, “definitions” and “examples”.
# For the first synset 'dog.n.01', which means the first noun sense of ‘dog’, we can first find all of its words/lemma names. 

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  "name": "Wordnet Interface"
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