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@benjamincohen1
Last active October 15, 2018 18:06
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{"nbformat_minor": 0, "cells": [{"source": "#Topic Modeling in Multi-Aspect Reviews\n###By Ben Cohen - bn.chn2 [at] gmail.com\nThe purpose of this project is to investigate topic modeling in multi-aspect reviews. More specifically, I wanted to investigate a way to find the words in reviews which were associated with the different categories being rated. \n\nSince I, like seemingly all data sciencists, love beer, I was thrilled to find a dataset containing about 1.5 million beer reviews from the beeradvocate website.\n\nBelow is a summary of my workflow and findings in playing around with this dataset.", "cell_type": "markdown", "metadata": {}}, {"source": "#Imports", "cell_type": "markdown", "metadata": {}}, {"execution_count": 1, "cell_type": "code", "source": "import pickle\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport glob\nfrom textblob import TextBlob\nfrom sklearn import linear_model\nimport numpy as np\n", "outputs": [], "metadata": {"collapsed": true, "trusted": false}}, {"source": "I did a bit of preprocessing on the data. The data came as a single (huge) .txt file. I defined a python class as seen below, and parsed the giant file into \"Review\" objects and saved each one as a pickled file in a directory called \"BeerData\".\n\nEach review came with a bunch of data, most of which was ignored for this project. There's definitely a lot of digging to do though!", "cell_type": "markdown", "metadata": {}}, {"execution_count": 2, "cell_type": "code", "source": "class Review(object):\n\tdef __init__(self, name, beerID, brewerID, abv, style, appearance, aroma, palate, taste, overall, time, reviewer, text):\n\t\tself.name = name\n\t\tself.beerID = beerID\n\t\tself.brewerID = brewerID\n\t\tself.abv = abv\n\t\tself.style = style\n\t\tself.appearance = appearance\n\t\tself.aroma = aroma\n\t\tself.palate = palate\n\t\tself.taste = taste\n\t\tself.overall = overall\n\t\tself.time = time\n\t\tself.reviewer = reviewer\n\t\tself.text = text\t", "outputs": [], "metadata": {"collapsed": true, "trusted": false}}, {"source": "Defining a mean function, which will be of use later on.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 3, "cell_type": "code", "source": "def mean(l):\n return sum(l)/float(len(l))", "outputs": [], "metadata": {"collapsed": true, "trusted": false}}, {"source": "As described earlier, our dataset contains about 1.5 million reviews. Below we read those files and create a list in memory with all of them.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 9, "cell_type": "code", "source": "print str(len(glob.glob(\"BeerData/*\"))) + \" total reviews\"", "outputs": [{"output_type": "stream", "name": "stdout", "text": "1518143 total reviews\n"}], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 4, "cell_type": "code", "source": "reviews = []\nfor review in glob.glob(\"BeerData/*\"):\n rv = pickle.load(open(review))\n reviews.append(rv)", "outputs": [], "metadata": {"collapsed": false, "trusted": false}}, {"source": "##Visualizations\nI wanted to get some idea of what my data looked like. Below, I plotted a bargraph of the distributions of each of the 4 rating aspects (taste, mouthfeel, smell, and appearance), as well as the overall score.\n\nAs expected, all of the distributions are roughly skewed left, having a mode of 4, and a mean of ~3.8. The only other interesting trend here is that taste seems to be the most polarizing individual aspect, and look the least. This is unsurprising, as taste is probably the most \"interesting\" aspect to most people, and look the least.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 83, "cell_type": "code", "source": "#overall\nallRatings = []\nratings = {z/2.: 0 for z in range(0,11)}\nfor x in reviews:\n allRatings.append(x.overall)\n ratings[x.overall] += 1\nprint ratings\n\nxs = [z/2.0 - .25 for z in range(0,11)]\nys = [ratings[z/2.0] for z in range(0,11)]\n\nprint \"Mean: \" + str(mean(allRatings))\nprint \"Std Dev: \" + str(np.std(allRatings))\nplt.bar(xs, ys, .5)\nplt.show()", "outputs": [{"output_type": "stream", "name": "stdout", "text": "{0.0: 7, 0.5: 0, 2.0: 35731, 3.0: 155807, 4.0: 559670, 5.0: 88955, 2.5: 54651, 1.0: 10209, 4.5: 314223, 3.5: 286858, 1.5: 12032}\nMean: 3.82390130574\nStd Dev: 0.717263904372\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x137f59d68>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 85, "cell_type": "code", "source": "#taste\nallRatings = []\n\nratings = {z/2.: 0 for z in range(0,11)}\n\nfor x in reviews:\n allRatings.append(x.taste)\n ratings[x.taste] += 1\nprint ratings\n\nxs = [z/2.0 - .25 for z in range(0,11)]\nys = [ratings[z/2.0] for z in range(0,11)]\nprint \"Mean: \" + str(mean(allRatings))\nprint \"Std Dev: \" + str(np.std(allRatings))\nplt.bar(xs, ys, .5)\nplt.show()", "outputs": [{"output_type": "stream", "name": "stdout", "text": "{0.0: 0, 0.5: 0, 2.0: 39050, 3.0: 156191, 4.0: 520352, 5.0: 82439, 2.5: 61854, 1.0: 9304, 4.5: 327623, 3.5: 307293, 1.5: 14037}\nMean: 3.80405501985\nStd Dev: 0.728596027051\n"}, {"output_type": "display_data", "data": {"image/png": 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eADuAa5IsT/I24APA4/PVLEk6fqk6+pWhJL8NfBv4HjA7+BZgF7CdwSf/fcCN7UYyST4J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\n/wDt/GIPRDJ8hAAAAABJRU5ErkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x182094eb8>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 87, "cell_type": "code", "source": "#smell\nallRatings = []\n\nratings = {z/2.: 0 for z in range(0,11)}\n\nfor x in reviews:\n allRatings.append(x.aroma)\n ratings[x.aroma] += 1\nprint ratings\n\nxs = [z/2.0 - .25 for z in range(0,11)]\nys = [ratings[z/2.0] for z in range(0,11)]\nprint \"Mean: \" + str(mean(allRatings))\nprint \"Std Dev: \" + str(np.std(allRatings))\nplt.bar(xs, ys, .5)\nplt.show()", "outputs": [{"output_type": "stream", "name": "stdout", "text": "{0.0: 0, 0.5: 0, 2.0: 39719, 3.0: 186909, 4.0: 537656, 5.0: 62879, 2.5: 61686, 1.0: 6483, 4.5: 264983, 3.5: 346118, 1.5: 11710}\nMean: 3.74621066658\nStd Dev: 0.695324116325\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0xea6950b8>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 88, "cell_type": "code", "source": "#look\nallRatings = []\n\nratings = {z/2.: 0 for z in range(0,11)}\n\nfor x in reviews:\n allRatings.append(x.appearance)\n ratings[x.appearance] += 1\nprint ratings\n\nxs = [z/2.0 - .25 for z in range(0,11)]\nys = [ratings[z/2.0] for z in range(0,11)]\nprint \"Mean: \" + str(mean(allRatings))\nprint \"Std Dev: \" + str(np.std(allRatings))\nplt.bar(xs, ys, .5)\nplt.show()", "outputs": [{"output_type": "stream", "name": "stdout", "text": "{0.0: 7, 0.5: 0, 2.0: 23784, 3.0: 154982, 4.0: 647242, 5.0: 64243, 2.5: 36706, 1.0: 3134, 4.5: 281181, 3.5: 301094, 1.5: 5770}\nMean: 3.85035829958\nStd Dev: 0.614305502717\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0xea7743c8>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 89, "cell_type": "code", "source": "#palate\nallRatings = []\n\nratings = {z/2.: 0 for z in range(0,11)}\n\nfor x in reviews:\n allRatings.append(x.palate)\n ratings[x.palate] += 1\nprint ratings\n\nxs = [z/2.0 - .25 for z in range(0,11)]\nys = [ratings[z/2.0] for z in range(0,11)]\nprint \"Mean: \" + str(mean(allRatings))\nprint \"Std Dev: \" + str(np.std(allRatings))\nplt.bar(xs, ys, .5)\nplt.show()", "outputs": [{"output_type": "stream", "name": "stdout", "text": "{0.0: 0, 0.5: 0, 2.0: 35582, 3.0: 193945, 4.0: 584018, 5.0: 61014, 2.5: 58406, 1.0: 6424, 4.5: 246941, 3.5: 321564, 1.5: 10249}\nMean: 3.75370567858\nStd Dev: 0.679330202401\n"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0xea77bba8>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "#Topic Modeling\nNow comes the fun part!\n\nMy thesis here is that the positive sentiment sentences in reviews where one aspect is much more positive than the others will likely be related to the aspect which got the high rating. In the below cells, we build to dictionaries.\n\nEach one has a list of sentences that our hypothesis says are assocated with each aspect. To build these, we look at reviews which have one aspect rated at least 1 full point higher than the second highest. We then classify each sentence's sentiment, using the \"TextBlob\" module, which is trained for general use, not for anything specific on our dataset.\n\nSentences with polarity above 0.2 (arbitrarily chosen) are added to our dictionary for further processing.\n\nWe then repeat the process for reviews with one aspect much worse than the rest, and look at sentences with negative sentiments and polarity < -0.2. ", "cell_type": "markdown", "metadata": {}}, {"execution_count": 15, "cell_type": "code", "source": "# ratings = {z/2.: 0 for z in range(0,11)}\nsentsDct = {x:[] for x in range(4)}\nfor x in reviews:\n scores = [x.aroma, x.taste, x.palate, x.appearance]\n sc2 = sorted([x.aroma, x.taste, x.palate, x.appearance], reverse=True)\n# if len([z for z in scores if z >= 4]) == 1 and len([z for z in scores if z <= 3]) == 3:\n if sc2[0] - sc2[1] >= 1:\n# goodScore = [z for z in scores if z >= 4][0]\n goodScore = sc2[0]\n #normalize\n x.text = x.text.lower()\n x.text = x.text.strip('\\(')\n x.text = x.text.strip('\\)')\n x.text = x.text.strip(',')\n x.text = x.text.strip('.')\n x.text = x.text.strip('-')\n x.text = x.text.strip('-')\n x.text = x.text.strip('\\t')\n x.text = x.text.strip('\\'')\n\n sents = x.text.split('.')\n\n for s in sents:\n blob = TextBlob(s)\n try:\n if blob.sentiment.polarity > .2:\n sentsDct[scores.index(goodScore)].append(s.strip()) \n except:\n pass\n\n\n", "outputs": [], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 90, "cell_type": "code", "source": "# ratings = {z/2.: 0 for z in range(0,11)}\nbadSentsDct = {x:[] for x in range(4)}\nfor x in reviews:\n scores = [x.aroma, x.taste, x.palate, x.appearance]\n sc2 = sorted([x.aroma, x.taste, x.palate, x.appearance])\n# if len([z for z in scores if z >= 4]) == 1 and len([z for z in scores if z <= 3]) == 3:\n if sc2[1] - sc2[0] >= 1:\n# goodScore = [z for z in scores if z >= 4][0]\n goodScore = sc2[0]\n #normalize\n x.text = x.text.lower()\n x.text = x.text.strip('\\(')\n x.text = x.text.strip('\\)')\n x.text = x.text.strip(',')\n x.text = x.text.strip('.')\n x.text = x.text.strip('-')\n x.text = x.text.strip('-')\n x.text = x.text.strip('\\t')\n x.text = x.text.strip('\\'')\n\n sents = x.text.split('.')\n\n for s in sents:\n blob = TextBlob(s)\n try:\n if blob.sentiment.polarity < -0.2:\n badSentsDct[scores.index(goodScore)].append(s.strip()) \n except:\n pass\n\n\n", "outputs": [], "metadata": {"collapsed": true, "trusted": false}}, {"source": "#Filtering\nWe then define a function which \"filters\" out words found in one category by looking at how commonly they occur in our other categories.\n\nPut another way, we use this method to keep words that are both positive or negative and specifically related to the aspect at hand (ie. piney for smell) and remove/lower words that have the correct sentiment, but are used across all of our categories (ie. wonderful may be used to describe a positive sentiment about a beer, but says nothing about a specific category) ", "cell_type": "markdown", "metadata": {}}, {"execution_count": 16, "cell_type": "code", "source": "def moreFreqwords(cl, others):\n ret = {}\n ln = float(len(cl))\n for word in cl.keys():\n pct = cl[word]/ln\n otherPcts = []\n for other in others:\n if word not in other:\n otherPcts.append(0.)\n else:\n otherPcts.append(other[word]/float(len(other)))\n if max(pct, max(otherPcts)) == pct:\n ret[word] = pct - mean(otherPcts)\n return ret", "outputs": [], "metadata": {"collapsed": false, "trusted": false}}, {"source": "#Finding related words\n\nIt's finally time to find the words that correspond to \"good\" and \"bad\" reviews for each aspect.\n\nThen, for each category, we do the following:\n+ Build up a frequency dictionary for each unique word used in those reviews (basically just keep count of how many times we've seen that word)\n+ Use our filtering method above to rank each word in this dictionary based on relative frequencies between our aspect and the other three.\n+ Sort this list based on frequency\n+ Check the part of speech of each of these words, and keep the adjectives (JJ), singular nouns (NN), comparative adjectives (JJR), and adverbs (RB)\n\nAfter this, we're done! I simply print out the top 50 words for each group. \n\nWe then repeat this for the other three aspects, followed by repeating the process for the \"bad\" reviews.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 60, "cell_type": "code", "source": "#good\n\ntaste = {}\nsmell = {}\nlook = {}\nfeel = {}\nfor sent in sentsDct[0]:\n sent = sent.split(' ')\n for word in sent:\n if word in smell:\n smell[word] += 1\n else:\n smell[word] = 1\n# smell.add(word)\n\nfor sent in sentsDct[1]:\n sent = sent.split(' ')\n for word in sent:\n if word in taste:\n taste[word] += 1\n else:\n taste[word] = 1\n\nfor sent in sentsDct[3]:\n sent = sent.split(' ')\n for word in sent:\n if word in look:\n look[word] += 1\n else:\n look[word] = 1\n\nfor sent in sentsDct[2]:\n sent = sent.split(' ')\n for word in sent:\n if word in feel:\n feel[word] += 1\n else:\n feel[word] = 1\n\n\nprint \"TASTE WORDS\"\nuniqueTasteWords = moreFreqwords(taste, [smell, look, feel]) #list(tasteWords - smellWords - lookWords - feelWords)\ngoodTaste = sorted(uniqueTasteWords, key=lambda x: taste[x], reverse=True)\nnewTaste = []\nfor w in goodTaste:\n try:\n if TextBlob(w).tags[0][1] in [\"NN\", \"JJ\", \"JJR\", \"RB\"]:\n newTaste.append(w)\n except:\n pass\nprint newTaste[:50]\n\n# print [p for p in sorted(uniqueTasteWords.keys(), key=lambda x: uniqueTasteWords[x], reverse=True) if TextBlob(p).tags[0][1] in [\"NN\", \"JJ\"]]\nuniqueSmellWords = moreFreqwords(smell, [taste, look, feel])\ngoodSmell = sorted(uniqueSmellWords, key=lambda x: smell[x], reverse=True)\nnewSmell = []\nfor w in goodSmell:\n try:\n if TextBlob(w).tags[0][1] in [\"NN\", \"JJ\", \"JJR\", \"RB\"]:\n newSmell.append(w)\n except:\n pass\nprint \"SMELL WORDS\"\nprint newSmell[:50]\n\nuniqueLookWords = moreFreqwords(look, [taste, smell, feel])\ngoodLook = sorted(uniqueLookWords, key=lambda x: uniqueLookWords[x], reverse=True)\nnewLook = []\nfor w in goodLook:\n try:\n if TextBlob(w).tags[0][1] in [\"NN\", \"JJ\", \"JJR\", \"RB\"]:\n newLook.append(w)\n except:\n pass\nprint \"LOOK WORDS\"\nprint newLook[:50]\n\n\nuniqueFeelWords = moreFreqwords(feel, [taste, look, smell])\ngoodFeel = sorted(uniqueFeelWords, key=lambda x: uniqueFeelWords[x], reverse=True)\nnewFeel = []\nfor w in goodFeel:\n try:\n if TextBlob(w).tags[0][1] in [\"NN\", \"JJ\", \"JJR\", \"RB\"]:\n newFeel.append(w)\n except:\n pass\nprint \"FEEL WORDS\"\nprint newFeel[:50]\n\n\n", "outputs": [{"output_type": "stream", "name": "stdout", "text": "TASTE WORDS\n['balanced', 'favorite', 'delicious', 'tasty', 'exceptional', 'perfectly', 'night', 'highly', 'sour', 'complex', 'truly', 'outstanding', 'job', 'heavy', 'flavorful', 'food', 'combination', 'wonderfully', 'beer!', 'balanced,', 'surprise', 'superb', 'satisfying', \"ipa's\", 'scotch', 'anyone', 'content', 'raisin', 'evening', 'goodness', 'complex,', 'subtle', 'growler', 'sierra', 'blended', 'area', 'excellent,', 'man', 'good!', 'sometimes', 'chocolatey', 'nevada', 'class', 'nutty,', 'future', 'smokiness', 'meal', 'everyone', 'flavorful,', 'delicious!']\nSMELL WORDS\n['aroma', 'smell', 'nose', 'fresh', 'fruit', 'pumpkin', 'wonderful', 'amazing', 'vanilla', 'unique', 'pine', 'aroma,', 'awesome', 'scent', 'part', 'grapefruit', 'smell,', 'tart', 'fantastic', 'banana', 'bourbon', 'apple', 'citrusy', 'oak', 'absolutely', 'pie', 'coffee,', 'blend', 'raspberry', 'cinnamon', 'incredible', 'vanilla,', 'citrus,', 'intense', 'tropical', 'smoke', 'overpowering', 'blueberry', 'powerful', 'apricot', 'initial', 'orange,', 'ginger', 'fruit,', 'funk', 'pineapple', 'grapefruit,', 'soon', 'barrel', 'simply']\nLOOK WORDS\n['head', 'light', 'good', 'beer', 'not', 'nice', 'more', 'sweet', 'very', 'color', 'malt', 'bit', 'pretty', 'glass', 'retention', 'taste', 'better', 'carbonation', 'white', 'too', 'just', 'flavor', 'great', 'much', 'beautiful', 'amber', 'body', 'mild', 'clear', 'brown', 'golden', 'bottle', 'strong', 'appearance', 'hop', 'lace', 'kind', 'medium', 'really', 'so', 'nothing', 'first', 'caramel', 'well', 'tan', 'dark', 'creamy', 'alcohol', 'top', 'bitterness']\nFEEL WORDS\n['mouthfeel', 'smooth', 'crisp', 'refreshing', 'smooth,', 'perfect', 'silky', 'mouthfeel,', 'mouth', 'm', 'texture', 'm-', 'creamy,', 'velvety', 'summer', 'tongue', 'incredibly', 'crisp,', 'full,', 'smoothness', 'creaminess', 'quenching', 'wheat,', 'chewy', 'crispness', 'refreshing,', 'thirst', 'palate,', \"beer's\", 'budweiser', 'mouthfeel/drinkability-', 'mouthfeel-', 'mouth,', 'miller', 'ideal', 'perfect,', 'luxurious', 'day,', 'exceptionally', 'feature', 'german', 'slick', 'silky,', 'viscous', 'water,', 'pinch', 'wheaty', 'corona', 'mouthfeel;', 'sensation']\n"}], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 94, "cell_type": "code", "source": "#bad\n\ntaste = {}\nsmell = {}\nlook = {}\nfeel = {}\nfor sent in badSentsDct[0]:\n sent = sent.split(' ')\n for word in sent:\n if word in smell:\n smell[word] += 1\n else:\n smell[word] = 1\n\nfor sent in badSentsDct[1]:\n sent = sent.split(' ')\n for word in sent:\n if word in taste:\n taste[word] += 1\n else:\n taste[word] = 1\n\nfor sent in badSentsDct[3]:\n sent = sent.split(' ')\n for word in sent:\n if word in look:\n look[word] += 1\n else:\n look[word] = 1\n\nfor sent in badSentsDct[2]:\n sent = sent.split(' ')\n for word in sent:\n if word in feel:\n feel[word] += 1\n else:\n feel[word] = 1\n\n\nprint \"TASTE WORDS\"\nuniqueTasteWords = moreFreqwords(taste, [smell, look, feel]) #list(tasteWords - smellWords - lookWords - feelWords)\ngoodTaste = sorted(uniqueTasteWords, key=lambda x: taste[x], reverse=True)\nnewTasteBad = []\nfor w in goodTaste:\n try:\n if TextBlob(w).tags[0][1] in [\"NN\", \"JJ\", \"JJR\", \"RB\"]:\n newTasteBad.append(w)\n except:\n pass\nprint newTasteBad[:50]\n\nuniqueSmellWords = moreFreqwords(smell, [taste, look, feel])\ngoodSmell = sorted(uniqueSmellWords, key=lambda x: smell[x], reverse=True)\nnewSmellBad = []\nfor w in goodSmell:\n try:\n if TextBlob(w).tags[0][1] in [\"NN\", \"JJ\", \"JJR\", \"RB\"]:\n newSmellBad.append(w)\n except:\n pass\nprint \"SMELL WORDS\"\nprint newSmellBad[:50]\n\nuniqueLookWords = moreFreqwords(look, [taste, smell, feel])\ngoodLook = sorted(uniqueLookWords, key=lambda x: uniqueLookWords[x], reverse=True)\nnewLookBad = []\nfor w in goodLook:\n try:\n if TextBlob(w).tags[0][1] in [\"NN\", \"JJ\", \"JJR\", \"RB\"]:\n newLookBad.append(w)\n except:\n pass\nprint \"LOOK WORDS\"\nprint newLookBad[:50]\n\n\nuniqueFeelWords = moreFreqwords(feel, [taste, look, smell])\ngoodFeel = sorted(uniqueFeelWords, key=lambda x: uniqueFeelWords[x], reverse=True)\nnewFeelBad = []\nfor w in goodFeel:\n try:\n if TextBlob(w).tags[0][1] in [\"NN\", \"JJ\", \"JJR\", \"RB\"]:\n newFeelBad.append(w)\n except:\n pass\nprint \"FEEL WORDS\"\nprint newFeelBad[:50]\n\n\n", "outputs": [{"output_type": "stream", "name": "stdout", "text": "TASTE WORDS\n['beer', 'taste', 'bad', 'just', 'flavor', 'good', 'bottle', 'so', 'really', 'unpleasant', 'disappointing', 'sure', 'awful', 'aftertaste', 'weird', 'wrong', 'maybe', 'something', 'bitterness', 'drink', 'drinkability', 'terrible', 'down', 'ever', 'even', 'brew', 'artificial', 'beer,', 'horrible', 'again', 'nasty', 'bitter', 'other', 'unfortunately', 'then', 'bad,', 'time', 'sour', 'unfortunately,', 'sorry', 'worse', 'disappointment', 'overall', 't', 'fruit', 'metallic', 'taste,', 'drinking', 'difficult', 'pretty']\nSMELL WORDS\n['not', 'very', 'smell', 'faint', 'aroma', 'thick', 'malt', 'nose', 'weak', 'subtle', 'hard', 'glass', 'hop', 'slight', 'cold', 'much', 'caramel', 'chocolate', 'sweetness', 'hint', 'nothing', 'thick,', 'creamy', 'here', 'ale', 'citrus', 'still', 'yeast', 'boring', 'stale', 'finger', 'malty', 'lot', 'past', 'faint,', 'aroma,', 'smell,', 'scent', 'malt,', 'anything', 'hardly', 'wheat', 'spice', 'character', 'drinkable', 'pint', 'grain', 'floral', 'end', 'else']\nLOOK WORDS\n['head', 'complex', 'color', 'retention', 'appearance', 'small', 'pale', 'poor', 'hazy', 'ring', 'cloudy', 'head,', 'brown', 'murky', 'base', 'ugly', 'lace', 'orange', 'off-white', 'amber', 'sediment', 'white', 'foam', 'minimal', 'dirty', 'alcohol', 'yellow', 'gold', 'dull', 'color,', 'immediately', 'well', 'hazy,', 'small,', 'barrel', 'glass,', 'amount', 'dangerously', 'dangerous', 'straw', 'delicate', 'wispy', 'abv', 'colour', 'pale,', 'insane', 'oak', 'cloudy,', 'rapidly', 'vanilla']\nFEEL WORDS\n['thin', 'mouthfeel', 'bit', 'body', 'too', 'watery', 'carbonation', 'thin,', 'little', 'm', 'dark', 'mouth', 'coffee', 'stout', 'style', 'black', 'm-', 'tan', 'palate', 'way', 'side', 'watery,', 'mouthfeel,', 'slowly', 'flat', 'somewhat', 'medium', 'quite', 'slightly', 'porter', 'body,', 'almost', 'touch', 'carbonation,', 'complaint', 'tongue', 'lack', 'tad', 'dry', 'fairly', 'bodied', 'chocolate,', 'imperial', 'feeling', 'slick', 'part', 'fizzy', 'water', 'stout,', 'only']\n"}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "We use this cell to print out the top 100 words for our categories for the purpose of making a slightly more interesing wordcloud.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 118, "cell_type": "code", "source": "print \" \".join(newLookBad[:100])\n", "outputs": [{"output_type": "stream", "name": "stdout", "text": "head complex color retention appearance small pale poor hazy ring cloudy head, brown murky base ugly lace orange off-white amber sediment white foam minimal dirty alcohol yellow gold dull color, immediately well hazy, small, barrel glass, amount dangerously dangerous straw delicate wispy abv colour pale, insane oak cloudy, rapidly vanilla tulip reddish attractive crazy liquid ugly, minute appearance, swirl honey complex, surface bottom retention, viscous warming deep late sipper soon murky, muddy caramel, collar blend tasty apple beige boy brownish mere buttery haze vanilla, bourbon spotty insanely blind lace, heavy oz crown amber, copper wonderfully opaque wisp orange, ripe visible\n"}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Now let's look at some pretty wordclouds of our findings. Important to note is that the sizes of words were determined randomly by the wordcloud generator (http://www.jasondavies.com/wordcloud/#) and mean nothing. For true ranking of aspects, see the output of the above cells.", "cell_type": "markdown", "metadata": {}}, {"source": "#Positive Aspects", "cell_type": "markdown", "metadata": {}}, {"execution_count": 125, "cell_type": "code", "source": "from IPython.display import SVG, Image\nSVG(filename='smell.svg')", "outputs": [{"execution_count": 125, "output_type": "execute_result", "data": {"text/plain": "<IPython.core.display.SVG object>", "image/svg+xml": "<svg height=\"600\" version=\"1.1\" width=\"960\" xmlns=\"http://www.w3.org/2000/svg\"><g/><g transform=\"translate(480,300)scale(1.0067113637924194,1.0067113637924194)\"><text style=\"font-size: 10px; font-family: Impact; fill: rgb(222, 158, 214);\" text-anchor=\"middle\" transform=\"translate(-51,0)rotate(6.666666507720948)\">delicious</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(140, 162, 82);\" text-anchor=\"middle\" transform=\"translate(7,156)rotate(-3.333333253860474)\">overpowering</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(231, 203, 148);\" text-anchor=\"middle\" transform=\"translate(139,-112)rotate(3.333333253860474)\">awesome</text><text style=\"font-size: 100px; font-family: Impact; fill: rgb(140, 162, 82);\" text-anchor=\"middle\" transform=\"translate(278,-57)\">smell</text><text style=\"font-size: 100px; font-family: Impact; fill: rgb(181, 207, 107);\" text-anchor=\"middle\" transform=\"translate(-115,-176)rotate(-10)scale(0.9999999999999999,0.9999999999999999)\">grapefruit</text><text style=\"font-size: 100px; font-family: Impact; fill: rgb(206, 219, 156);\" text-anchor=\"middle\" transform=\"translate(-229,175)rotate(6.666666507720948)\">pumpkin</text><text style=\"font-size: 100px; font-family: Impact; fill: rgb(140, 109, 49);\" text-anchor=\"middle\" transform=\"translate(84,5)rotate(10)scale(0.9999999999999999,0.9999999999999999)\">amazing</text><text style=\"font-size: 100px; font-family: Impact; fill: rgb(189, 158, 57);\" text-anchor=\"middle\" transform=\"translate(140,138)rotate(-3.3333332538604745)\">vanilla</text><text style=\"font-size: 100px; font-family: Impact; fill: rgb(231, 186, 82);\" text-anchor=\"middle\" transform=\"translate(-174,10)rotate(-3.333333253860474)\">pine</text><text style=\"font-size: 100px; font-family: Impact; fill: rgb(231, 203, 148);\" text-anchor=\"middle\" transform=\"translate(-202,95)rotate(-3.3333332538604736)scale(1,1)\">fruit</text><text style=\"font-size: 100px; font-family: Impact; fill: rgb(132, 60, 57);\" text-anchor=\"middle\" transform=\"translate(-90,-87)rotate(-6.666666507720948)scale(1,1)\">banana</text><text style=\"font-size: 100px; font-family: Impact; fill: rgb(173, 73, 74);\" text-anchor=\"middle\" transform=\"translate(214,-141)rotate(6.666666507720948)scale(1,1)\">oak</text><text style=\"font-size: 100px; font-family: Impact; fill: rgb(231, 150, 156);\" text-anchor=\"middle\" transform=\"translate(237,-220)rotate(3.333333253860474)\">aroma</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(165, 81, 148);\" text-anchor=\"middle\" transform=\"translate(105,-64)rotate(-6.666666507720949)\">barrel</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(206, 109, 189);\" text-anchor=\"middle\" transform=\"translate(-180,-61)rotate(-6.666666507720947)scale(1,1)\">part</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(222, 158, 214);\" text-anchor=\"middle\" transform=\"translate(158,46)rotate(10)scale(0.9999999999999999,0.9999999999999999)\">wonderful</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(57, 59, 121);\" text-anchor=\"middle\" transform=\"translate(-27,57)rotate(10)scale(0.9999999999999999,0.9999999999999999)\">tart</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(82, 84, 163);\" text-anchor=\"middle\" transform=\"translate(25,6)rotate(3.3333332538604745)\">fantastic</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(107, 110, 207);\" text-anchor=\"middle\" transform=\"translate(6,-82)rotate(-10)scale(0.9999999999999999,0.9999999999999999)\">fresh</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(156, 158, 222);\" text-anchor=\"middle\" transform=\"translate(-81,47)rotate(6.666666507720948)\">bourbon</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(99, 121, 57);\" text-anchor=\"middle\" transform=\"translate(-53,108)rotate(0)\">apple</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(140, 162, 82);\" text-anchor=\"middle\" transform=\"translate(-48,121)rotate(-6.666666507720949)\">citrusy</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(181, 207, 107);\" text-anchor=\"middle\" transform=\"translate(114,151)rotate(-3.3333332538604736)scale(1,1)\">absolutely</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(206, 219, 156);\" text-anchor=\"middle\" transform=\"translate(206,-48)rotate(10)scale(0.9999999999999999,0.9999999999999999)\">pie</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(140, 109, 49);\" text-anchor=\"middle\" transform=\"translate(75,-145)rotate(-10)scale(0.9999999999999999,0.9999999999999999)\">coffee</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(189, 158, 57);\" text-anchor=\"middle\" transform=\"translate(-13,4)rotate(-3.333333253860474)\">blend</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(231, 186, 82);\" text-anchor=\"middle\" transform=\"translate(238,-41)rotate(-6.666666507720948)\">raspberry</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(231, 203, 148);\" text-anchor=\"middle\" transform=\"translate(68,155)rotate(10)scale(0.9999999999999999,0.9999999999999999)\">unique</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(132, 60, 57);\" text-anchor=\"middle\" transform=\"translate(-294,9)rotate(10)scale(0.9999999999999999,0.9999999999999999)\">incredible</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(173, 73, 74);\" text-anchor=\"middle\" transform=\"translate(103,71)rotate(0)\">citrus</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(214, 97, 107);\" text-anchor=\"middle\" transform=\"translate(155,-137)rotate(6.666666507720948)\">intense</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(231, 150, 156);\" text-anchor=\"middle\" transform=\"translate(-219,25)rotate(3.3333332538604736)scale(1,1)\">tropical</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(123, 65, 115);\" text-anchor=\"middle\" transform=\"translate(66,51)rotate(-6.666666507720947)scale(1,1)\">smoke</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(165, 81, 148);\" text-anchor=\"middle\" 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malformed\nImage(filename='lookBad.png')\n", "outputs": [{"execution_count": 124, "output_type": "execute_result", "data": {"image/png": 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BZTsm6vFYadAp7ys9mFgEmOqwpXx9HTSE7H1+0zydIip8kcp34HOe5P3C1RkB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\npmemuTg4eAis9Vaq1QlSOhqkdKaVPEr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gCf2D8QO+00nj/w/ld+LU901DBlZRkN50xipW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\nvx2AEmnjkoeYLNIkCeyPL3aZ4nxBYmiqJDJ9WESwbao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Bqa4FkJvQyyg378rPn26dmlL3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K68uVGnqPKlx7oVbXPFYFM8883MLt27pKJdneucTiRkMZWF1fmXYGOKNgK40vSEaV8syHGW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\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84, 163);\" text-anchor=\"middle\" transform=\"translate(-203,36)rotate(6.666666666666664)\">texture</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(82, 84, 163);\" text-anchor=\"middle\" transform=\"translate(228,104)rotate(10)\">oatmeal</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(107, 110, 207);\" text-anchor=\"middle\" transform=\"translate(97,83)rotate(6.666666666666664)\">coating</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(156, 158, 222);\" text-anchor=\"middle\" transform=\"translate(268,5)rotate(-6.666666666666667)\">excessive</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(99, 121, 57);\" text-anchor=\"middle\" transform=\"translate(255,53)rotate(6.666666666666664)\">bubbly</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(140, 162, 82);\" text-anchor=\"middle\" transform=\"translate(59,-173)rotate(-3.333333333333334)\">roast</text><text style=\"font-size: 10px; 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font-family: Impact; fill: rgb(107, 110, 207);\" text-anchor=\"middle\" transform=\"translate(40,79)rotate(0)\">dusty</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(123, 65, 115);\" text-anchor=\"middle\" transform=\"translate(-58,-61)rotate(-10)\">smoke</text><text style=\"font-size: 10px; font-family: Impact; fill: rgb(82, 84, 163);\" text-anchor=\"middle\" transform=\"translate(-23,-82)rotate(10)\">little</text></g></svg>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "#Analysis\nTo me, these look great! Almost all of the words seem relevant to their specific aspect, and they seem like they have the right polarity.\n\nThat said, I wanted a slightly more complex analysis than \"well, it looks good\". Below, I build a basic linear regression classifier trained on the first 50 words from each group, and attemp to use this to predict the overall score. \n\nI don't explore this very fully at all, but demonstrate that it beats the baseline system (assigning every beer the mean score). In future work, I plan to explore possible uses for these words, including individual aspect prediction.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 19, "cell_type": "code", "source": "def features(txt, keyWordsLst):\n ln = float(len(txt.split(' ')))\n txt = txt.lower()\n txt = txt.strip(',')\n txt = txt.strip('.')\n txt = txt.strip(')')\n txt = txt.strip('(')\n txt = txt.strip('-')\n vec = []\n\n for word in keyWordsLst:\n# print word\n vec.append(txt.count(word)/ln)\n# print vec\n return vec", "outputs": [], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 22, "cell_type": "code", "source": "import random\nrandom.shuffle(reviews)\n", "outputs": [], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 75, "cell_type": "code", "source": "keyWords = list(newFeel[:50] + newLook[:50] + newTaste[:50] + newSmell[:50]\\\n + newFeelBad[:50] + newLookBad[:50] + newTasteBad[:50] + newSmellBad[:50])\nxs, ys = [], []\nfor review in reviews[:100000]:\n vec = features(review.text, keyWords)\n xs.append(vec)\n ys.append(review.overall)\n \nregr = linear_model.LinearRegression()\nregr.fit(xs, ys)\n", "outputs": [{"execution_count": 75, "output_type": "execute_result", "data": {"text/plain": "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 46, "cell_type": "code", "source": "def dist(x,y): \n return np.sqrt(np.sum((x-y)**2))\n\n", "outputs": [], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 128, "cell_type": "code", "source": "diffs = []\n \nfor review in reviews[100000:101000]:\n vec = features(review.text, keyWords)\n pre = regr.predict(vec)\n\n x = review\n\n diff = abs(x.overall - pre)**2\n\n diffs.append(diff)\nprint \"Mean difference: \" + str(mean(diffs))\n", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Mean difference: 0.421655982286\n"}], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 129, "cell_type": "code", "source": "\ndiffs = []\n \nfor review in reviews[100000:101000]:\n\n pre = 3.82\n x = review\n\n diff = abs(x.overall - pre)**2\n\n diffs.append(diff)\nprint \"Mean difference: \" + str(mean(diffs))\n", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Mean difference: 0.50582\n"}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "#Conclusions and Future work\nAbove we took a look at a very simple, yet seemingly very effective way to isolate words that indicate either a positive or negative sentiment about a single aspect of a multi-aspect review. Additionally, we do so in an unsupervised manner (ie. no seed words, word or sentence labeling are required). Additionally, we apply no domain specific knowledge, leading me to believe that this method will generalize well to other datasets/tasks.\n\nIn future work, I hope to:\n+ Look at domain specific sentiment analysis models to improve sentiment tagging.\n+ Use this to build a more robust and accurate prediction model.", "cell_type": "markdown", "metadata": {"collapsed": true}}, {"source": "#License \n\nCopyright (c) 2015, Ben Cohen All rights reserved.\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, pulverize, distribute, synergize, compost, defenestrate, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nIf the Author of the Software (the \"Author\") needs a place to crash and you have a sofa available, you should maybe give the Author a break and let him sleep on your couch.\n\nIf you are caught in a dire situation wherein you only have enough time to save one person out of a group, and the Author is a member of that group, you must save the Author.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO BLAH BLAH BLAH ISN'T IT FUNNY HOW UPPER-CASE MAKES IT SOUND LIKE THE LICENSE IS ANGRY AND SHOUTING AT YOU.", "cell_type": "markdown", "metadata": {}}, {"execution_count": null, "cell_type": "code", "source": "", "outputs": [], "metadata": {"collapsed": true, "trusted": false}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 2", "name": "python2", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "2.7.7", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython2", "codemirror_mode": {"version": 2, "name": "ipython"}}}}
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