code/nb5_modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from datetime import datetime\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk import word_tokenize, FreqDist\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import string\n",
    "import re\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, plot_confusion_matrix, classification_report\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.pipeline import Pipeline \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet_tokenizer = TweetTokenizer(preserve_case=True, strip_handles=True, reduce_len=True)\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "def tokenize_lemmatize_tweet(tweet):\n",
    "    ''' \n",
    "    Tokenize then Lemmatize the given tweet \n",
    "    '''\n",
    "    #remove links\n",
    "    tweet = re.sub(r'http\\S+|www\\S+|https\\S+', '', tweet, flags=re.MULTILINE)\n",
    "    #tokenize\n",
    "    tokens = tweet_tokenizer.tokenize(tweet)\n",
    "    #lemmatize\n",
    "    lemmatized_tokens = [lemmatizer.lemmatize(token) for token in tokens]\n",
    "    return lemmatized_tokens\n",
    "\n",
    "\n",
    "def calc_and_display_performance_metric(the_estimator,\n",
    "                                        Xtest, \n",
    "                                        ytest, \n",
    "                                        Xtrain, \n",
    "                                        ytrain, \n",
    "                                        classifier_predictions, \n",
    "                                        cm_title, \n",
    "                                        cm_normalize='true'):\n",
    "    '''\n",
    "    Calcuate and display peformance metrics for a classifier\n",
    "    '''\n",
    "    # Metrics - on how well the classifer performed on test data \n",
    "    # AND getting metrics on training data to check for model overfitting.\n",
    "    train_predictions = the_estimator.predict(Xtrain)\n",
    "    show_peformance_metrics(ytrain, train_predictions, Xtest, ytest, classifier_predictions)\n",
    "    # Confusion Matrix\n",
    "    plot_pretty_cf(the_estimator, Xtest, ytest, cm_normalize, cm_title)\n",
    "\n",
    "\n",
    "def show_peformance_metrics(y_train, \n",
    "                            y_train_pred, \n",
    "                            X_test, \n",
    "                            y_test, \n",
    "                            y_pred):\n",
    "    ''' \n",
    "    Print out peformance metrics\n",
    "    '''\n",
    "    train_acc_core = accuracy_score(y_train, y_train_pred)\n",
    "    test_acc_score = accuracy_score(y_test, y_pred)\n",
    "\n",
    "    train_wf1_score = f1_score(y_train, y_train_pred, average='weighted')\n",
    "    test_wf1_score = f1_score(y_test, y_pred, average='weighted')\n",
    "    print('--------------------------------------------------------------------------')\n",
    "    print('Training Accuracy:{:.4}\\t\\t Testing Accuracy: {:.4}'.format(train_acc_core, test_acc_score))\n",
    "    print('Training Weighted F1:{:.4}\\t\\t Testing Weighted F1: {:.4}'.format(train_wf1_score, test_wf1_score))\n",
    "    print('--------------------------------------------------------------------------')\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    print('--------------------------------------------------------------------------')\n",
    "\n",
    "\n",
    "def plot_pretty_cf(the_estimator,\n",
    "                   Xtest, \n",
    "                   ytest, \n",
    "                   cm_normalize, \n",
    "                   title):\n",
    "    ''' Plot a pretty confusion matrix '''\n",
    "    fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "    plot_confusion_matrix(the_estimator, \n",
    "                          Xtest, \n",
    "                          ytest, \n",
    "                          cmap='Blues',\n",
    "                          normalize=cm_normalize, \n",
    "                          ax=ax)\n",
    "\n",
    "    ax.set_title(title, size='large')\n",
    "    ax.set_xlabel('Predicted Label', size='medium')\n",
    "    ax.set_ylabel('True Label', size='medium')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "us_only_tweets = pd.read_csv('../data/vetted_usa_tweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "positive    22618\n",
       "neutral      4708\n",
       "negative     3273\n",
       "Name: sentiment, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_only_tweets['sentiment'].value_counts()\n",
    "# We have class imbalance here with MANY more positive tweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling Steps:\n",
    "\n",
    "* Create TRAIN and TEST data sets\n",
    "\n",
    "* Create a Pipeline to use GridSearchCV to find best hyperparameters for LinearSVC, and fit the best LinearSVC model\n",
    "    * Apply light text processing and vectorize TRAIN data using TF-IDF \n",
    "    * Use 'vanilla' classification model\n",
    "    * Define parameter grid for consideration by GridSearchCV\n",
    "    * Use GridSearchCV to peform hyperparameter tuning to fit/train the best model\n",
    "\n",
    "* Create predictions for TEST set\n",
    "\n",
    "* Examine performance metrics: Weighted F1 Scores\n",
    "    * Compare the performance metrics for TRAIN and TEST  (If model does __much__ better on TRAIN than on TEST set, then we’re likely overfitting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create TRAIN and TEST data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = us_only_tweets['content']\n",
    "y = us_only_tweets['sentiment']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text Processing\n",
    "\n",
    "Vectorize the Tweets using TF-IDF\n",
    "* Using TF-IDF to vectorize tweets.\n",
    "    * TD-IDF focuss on words that appear heavily in tweets from a given class, but rarely appear in tweets from other class/classes.\n",
    "* Using a custom function as tokenzier (tokenize_lemmatize_tweet)\n",
    "    * URLs will be removed using a regex\n",
    "    * TweetTokenier will tokenize the Tweets - usernames/handles will be stripped, case will be retained, and repeated characters in words will be reduced (ex: waaaay to way)\n",
    "    *Wordnet Lemmatizer on the resulting tokens\n",
    "* Removing english stopwords\n",
    "    * __Not__ removing/stripping punctuation as it may be useful for sentiment classifification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(tokenizer=tokenize_lemmatize_tweet,\n",
    "                             stop_words=stopwords.words('english'),\n",
    "                             decode_error='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Honing in on the 'best' text classifer\n",
    "\n",
    "In the model_playground.ipynb, I experimented with the following:\n",
    "* RandomForestClassifier\n",
    "* SGDClassifier\n",
    "* LinearSVC\n",
    "\n",
    "The LinearSVC was the most promising, so I'll focus on creating the 'best' classification model using LinearSVC.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------\n",
      "Training Accuracy:0.9951\t\t Testing Accuracy: 0.924\n",
      "Training Weighted F1:0.9952\t\t Testing Weighted F1: 0.923\n",
      "--------------------------------------------------------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.83      0.77      0.80       953\n",
      "     neutral       0.87      0.83      0.85      1420\n",
      "    positive       0.95      0.97      0.96      6807\n",
      "\n",
      "    accuracy                           0.92      9180\n",
      "   macro avg       0.88      0.85      0.87      9180\n",
      "weighted avg       0.92      0.92      0.92      9180\n",
      "\n",
      "--------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear SVC with no hyperparameter tuning\n",
    "not_tuned_lsvc_clf = Pipeline([('tfidf', vectorizer),\n",
    "                               ('clf', LinearSVC(class_weight='balanced', max_iter=2000))\n",
    "                               ])\n",
    "\n",
    "not_tuned_lsvc_clf.fit(X_train, y_train)\n",
    "predictions = not_tuned_lsvc_clf.predict(X_test)\n",
    "\n",
    "calc_and_display_performance_metric(the_estimator=not_tuned_lsvc_clf, \n",
    "                                    Xtest=X_test, \n",
    "                                    ytest=y_test,\n",
    "                                    Xtrain=X_train,\n",
    "                                    ytrain=y_train,\n",
    "                                    classifier_predictions=predictions,\n",
    "                                    cm_title='Untuned Linear SVC Confusion Matrix',\n",
    "                                    cm_normalize='true')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning\n",
    "Tune using GridSearchCV to find the best Hyperparameters\n",
    "* Using weighted F1 as the performance metric\n",
    "* using setting class_weight to be 'balanced' as we have class imbalance with __MANY__ more positive tweets than negative or neutral."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 48 candidates, totalling 480 fits\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:  2.1min\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:  8.5min\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed: 20.5min\n",
      "[Parallel(n_jobs=-1)]: Done 480 out of 480 | elapsed: 23.8min finished\n",
      "0.9386883242927674\n",
      "{'estimator__C': 1, 'estimator__class_weight': 'balanced', 'estimator__dual': False, 'estimator__loss': 'squared_hinge', 'estimator__max_iter': 1000, 'estimator__penalty': 'l1', 'estimator__random_state': 42}\n"
     ]
    }
   ],
   "source": [
    "# Linear SVC was very promising!  Let's perform hyperparameter tuning with GridSearchCV!\n",
    "\n",
    "lsvc_pipeline = Pipeline([ ('tfidf', vectorizer), ('estimator', LinearSVC()) ])\n",
    "\n",
    "p_grid = {'estimator__penalty': ['l1', 'l2'],\n",
    "          'estimator__class_weight': ['balanced'],\n",
    "          'estimator__random_state': [42],\n",
    "          'estimator__loss': ['hinge', 'squared_hinge'],\n",
    "          'estimator__dual': [True, False],\n",
    "          'estimator__C' : [.1, 1, 10],\n",
    "          'estimator__max_iter' : [1000, 2000]\n",
    "          }\n",
    "\n",
    "#using 10 fold cross validation instead of default of 5\n",
    "grid_lsvc = GridSearchCV(estimator=lsvc_pipeline,\n",
    "                          param_grid=p_grid,\n",
    "                          scoring='f1_weighted',\n",
    "                          verbose=1,\n",
    "                          n_jobs=-1,\n",
    "                          cv=10)\n",
    "\n",
    "grid_lsvc.fit(X_train, y_train)\n",
    "best_lsvc_clf = grid_lsvc.best_estimator_\n",
    "print(grid_lsvc.best_score_)\n",
    "print(grid_lsvc.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------\n",
      "Training Accuracy:0.9827\t\t Testing Accuracy: 0.9423\n",
      "Training Weighted F1:0.9828\t\t Testing Weighted F1: 0.9423\n",
      "--------------------------------------------------------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.86      0.80      0.83       953\n",
      "     neutral       0.86      0.95      0.90      1420\n",
      "    positive       0.97      0.96      0.97      6807\n",
      "\n",
      "    accuracy                           0.94      9180\n",
      "   macro avg       0.90      0.90      0.90      9180\n",
      "weighted avg       0.94      0.94      0.94      9180\n",
      "\n",
      "--------------------------------------------------------------------------\n"
     ]
    },
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   "source": [
    "predictions = best_lsvc_clf.predict(X_test)\n",
    "\n",
    "calc_and_display_performance_metric(the_estimator=best_lsvc_clf, \n",
    "                                    Xtest=X_test, \n",
    "                                    ytest=y_test,\n",
    "                                    Xtrain=X_train,\n",
    "                                    ytrain=y_train,\n",
    "                                    classifier_predictions=predictions,\n",
    "                                    cm_title='Tuned Linear SVC Confusion Matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model performance evaluation\n",
    "\n",
    "* The tuned LinearSVC model has a weighted F1 score of __0.94__ which is very good performance\n",
    "* Let's examine the F1 score (and total number of tweets) for each class:\n",
    "    * Positive class has F1 score of 0.97 (no surprise as this the majority class) \n",
    "    * Neutral class has F1 score of 0.90\n",
    "    * Negative class has F1 score of 0.83\n",
    "* The lowest F1 score is for the least represented class, Negative. However, the overall Weighted F1 score of 0.94 is very acceptable.\n",
    "* The model does not appear to suffer from overfitting.  The Train score is higher than the Test score, but not __substaintially__ higher.\n",
    "    * Train F1 Score: __0.98__\n",
    "    * Test F1 Score: __0.94__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the Trained Classifer\n",
    "Now that we've used Grid Search CV to determine the best hyperparameters for the LinearSVC model and have trained it. Pickle the trained model for use with new data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dateTimeObj = datetime.now()\n",
    "timestampStr = dateTimeObj.strftime('%d_%b_%Y_%H_%M')\n",
    "timestamped_model_filename = 'fitted_lsvc_model_' + timestampStr + '.pickle'\n",
    "\n",
    "with open(timestamped_model_filename, 'wb') as modelFile:\n",
    "    pickle.dump(best_lsvc_clf, modelFile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use the Classifier on new data - the data comes from Tweets that are NOT in the Training or Test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load pre-trained model - no need to retrain it again\n",
    "with open(timestamped_model_filename, 'rb') as modelFile:\n",
    "    stored_model = pickle.load(modelFile)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['negative', 'positive', 'negative', 'positive', 'positive',\n",
       "       'neutral'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = {'content': \n",
    "    ['@jac___13 Sorry Jackie! I’ve kept our kids fully remote, even opting out of the hybrid model. I just can’t trust other outside of my bubble right now. At least you guys in K-12 are in the earlier phases to get vaccinated. My wife in higher-Ed is not. I truly feel for educators right now.', \n",
    "    'Parents, Grandparents, and Guardians! Explore Pre-K and K-12 options, tour schools and chat LIVE with school representatives during our Virtual School Fair.\\nhttps://t.co/ogqDR4m52K', \n",
    "    'Screw the open schools COVID movement for damaging the term open schools.\\n\\nAn Open School is a K-12 self-directed democratic school.\\nJCOS in Denver Colorado is the last Open School in USA\\n\\nHere\\'s an EP we released w/ a former student &amp; teacher.\\n\\nhttps://t.co/3hJ2sGwMLD https://t.co/jbIfpO80UP', \n",
    "    'How does creating a federally supported K-12 to postsecondary education pathway help America recover from the COVID-19 pandemic? \\n\\nRead the new @amprog report to learn more: https://t.co/mCTe84WFtO #CAPEducation', \n",
    "    'Parents! 🗣️ @DaytonLive365 is offering a free “virtual field trip” for local K-12 students from Feb. 1 to March 14. Register here for access to stream Grammy-nominated duo, Black Violin: https://t.co/XR29EKm2TT https://t.co/mCb6W2vUZp',\n",
    "    'There are two parts to the bill; the second would require districts to offer a 5 day per week, in-person option for all students, but only *after* the completion of vaccination phase 1b.  K-12 teachers and staff are included in SDMAC\\'s 1b plan.'\n",
    "    ] }\n",
    "\n",
    "new_data_df = pd.DataFrame.from_dict(new_data)\n",
    "\n",
    "stored_model.predict(new_data_df['content'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion: Good performance on this final model!\n",
    "\n",
    "The predictions for the new data seem reasonable. All 3 sentiment classes were detected. Even the negative sentiment, which is the least represented sentiment class in the Training/Test data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recommendation:\n",
    "* For other Data Scientists seeking to expand upon this work:  The collected data has a class imbalance issue with 73.9% of the data labeled as Positive. Make sure any hyperparameter tuning uses the 'balanced' class_weight for LinearSVC or any other classification algorithm you may choose to experiment with. If class_weight 'balanced' is not an option, consider using RandomOverSampling to address the imbalance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Future Work:\n",
    "* Create a dashboard/app to classify tweets from the live Twitter stream \n",
    "    * Allow user to set for entire United States, selected US region(s) or selected US state(s)\n",
    "* Continue to collect Tweets over a longer timespan and update model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Conclusion\n",
    "This is the final noteboook in the __Learning During COVID__ project. Let's review the work peformed in the Project Notebooks, then present Recommendations and Future Work.\n",
    "\n",
    "1. I collected data from Twitter using [Tweepy](https://github.com/tweepy) and combined my collected data with the [Tweets about distance learning](https://www.kaggle.com/barishasdemir/tweets-about-distance-learning) dataset.\n",
    "2. I applied Positive, Negative, and Neutral sentiment labels to the Tweets using a hybrid approach, combining the [VADER](https://github.com/cjhutto/vaderSentiment) and [Text Blob](https://github.com/sloria/textblob) sentiment tools with (limited) human labeling. 30,599 Tweets made it to the final project dataset after combining the Kaggle and collected Tweets, filtering out non-US locations, and dropping Tweets where the Sentiment Tools did NOT agree.\n",
    "3. I performed [EDA](eda.ipynb) on various sentiment breakdowns. I also performed [Corpus EDA](corpus_eda.ipynb) using Unsupervised Learning (LatentDirichletAllocation) to perform Topic Modeling.\n",
    "4. I trained three different multiclass classifers (RandomForest, SGDClassifier, and LinearSCV) in the [Model Experimentation Notebook](model_playground.ipynb), using weighted F1 score as the performance metric, and ultimately selected the LinearSVC model for further tuning.\n",
    "5. In this final model notebook, I performed hyperparameter tuning using GridSearchCV and trained in a multiclass classifer with a Weighted F1 score of __0.94__ \n",
    "\n",
    "\n",
    "# Recommendations\n",
    "My recommendations are for the Education-focused Stakeholders identified in the Introduction and for Data Scientists seeking to expand upon this work.\n",
    "\n",
    "### Recommendation 1\n",
    "* Audience: Education-focused Stakeholders\n",
    "* The number of Positive sentiment Tweets is __substantially__ higher than the other sentiment classes in every State, in every Region. This initial analysis suggests that Twitter is being used across the US to communicate positive information and statements on Education during COVID. I recommend that Stakeholders explore the [Topic Visualization with pyLDAvis Notebook](https://nbviewer.jupyter.org/github/lspope/capstone/blob/main/code/demo_topic_viz.ipynb#topic=3&lambda=1&term=) to explore the topics detected across the US and within each US geographic Region.\n",
    "\n",
    "### Recommendation 2\n",
    "* Audience: Education-focused Stakeholders\n",
    "* EDA on the breakdown of Tweets by State in each Region identified two areas of concern. \n",
    "    * __2 of the 5__ Regions have a 'leading' State that far exceeds the others in number of Tweets. The leading state in the _West_ and _Southwest_ Regions have more than __twice__ the number of Tweets of the 'next-in-line' State I Recommend that Education-focused Stakeholders take into consideration that these Regions may be over-representing the 'leading' state. Region/State population analysis is most likely required.\n",
    "    * __3 of the 5__ Regions had States that provided > 1% of Tweets for the Region. This occured in the _West_ (Montana, Alaska, and Wyoming), _Northeast_ (Delaware), and _Midwest_ (North and South Dakota). I recommend that Stakeholders take into consideration that these States, in particular, may be under-represented in the Region. State population analysis is most likely required.\n",
    "\n",
    "### Recommendation 3\n",
    "* Audience: Data Scientists seeking to expand this work\n",
    "* The collected data has a class imbalance issue with 73.9% of the data labeled as Positive. Make sure any hyperparameter tuning uses the 'balanced' class_weight for LinearSVC or any other classification algorithm you may choose to experiment with. If class_weight 'balanced' is not an option, consider using Random Over Sampling to address the imbalance.\n",
    "\n",
    "# Future Work:\n",
    "* Create a dashboard/app that would allow the user to select specific Regions and/or States for Topic Modeling. Also allow for setting the desired number of topics and top word count. \n",
    "* Create a dashboard/app to classify Tweets from the live Twitter stream. Allow the user to stream for Tweets for the entire United States, or selected US Region(s) or selected US State(s).\n",
    "* Create a dashboard/app that would allow the user to select specific Regions and/or States for Topic Modeling. Also allow for setting the desired number of topics and top word count. \n",
    "* Identify specific Topics of interest and train a Text Classifier to classify Tweets according to those Topics. Consider Sentiment Classification after Tweet Topic has been determined.\n",
    "    * For Example: Create a Text Classifer with Educators in mind.  Detect Tweets on Topics such as Online Teaching Resources, Participation/Engagment Tips for Online Learning, Teacher Professional Development, EdTech Products.\n",
    "* Continue to collect Tweets over a longer timespan and update model.\n",
    "* Improve the regex code used to detect a 'likely' United States location from the Tweet User-provided Location string.\n",
    "\n",
    "\n",
    "# For More Information\n",
    "* Review the [non-technical presentation](../CapstoneProject_LeahPope.pdf)\n",
    "* Contact the author [Leah Pope](https://www.linkedin.com/in/leahspope/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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