{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IAC - 取得潛在IAC\n",
    "1. InputData(input_path) : load評論資料(.csv or .xlsx)\n",
    "2. GetIAC() : 抓潛在IAC\n",
    "3. Save(output_path) : 存檔"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from multiprocessing.dummy import Pool as ThreadPool\n",
    "import Get_all_IAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input success\n"
     ]
    }
   ],
   "source": [
    "IAC = Get_all_IAC.Get_IAC()\n",
    "IAC.InputData('LampSet/LampSet.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3731/3731 [02:34<00:00, 24.09it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Get IAC success.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "IAC.GetIAC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pool = ThreadPool(8)\n",
    "results = pool.map(IAC.GetIAC(), IAC.sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success save\n"
     ]
    }
   ],
   "source": [
    "IAC.Save('LampSet/IAC.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Word2Vec - 訓練Word2Vec模型\n",
    "1. 初始呼叫時就會將原有的所有評論load進來\n",
    "2. add_data(input_path) : 加進新的資料\n",
    "3. Train(dimension, min_word_count, context_window) : 訓練\n",
    "4. Save(output_path) : 存檔"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import Train_Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1197768/1197768 [32:10<00:00, 620.33it/s]\n"
     ]
    }
   ],
   "source": [
    "train = Train_Word2Vec.Train_Word2vec()\n",
    "train.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.add_data('dataset/dataset/LampSet.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model...\n",
      "Finsih!\n"
     ]
    }
   ],
   "source": [
    "train.Train(500, 10, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('large', 0.8205270767211914),\n",
       " ('tiny', 0.7073460817337036),\n",
       " ('smallish', 0.5926549434661865),\n",
       " ('smaller', 0.5902241468429565),\n",
       " ('narrow', 0.5846545100212097),\n",
       " ('tall', 0.572284460067749),\n",
       " ('wide', 0.5643041133880615),\n",
       " ('shallow', 0.5634061098098755),\n",
       " ('larger', 0.5421552658081055),\n",
       " ('big', 0.5320311784744263)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.model.most_similar('small')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.nlp.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.Save('LampSet/word2vec')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Auto Keyword Extract - 自動分類\n",
    "1. AutoLabeling(input_path) : 自動分類，input IAC\n",
    "2. Save(output_path) : 存檔"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import Auto_Keyword_Extract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "label = Auto_Keyword_Extract.Auto_Keyword_Extract()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frustrated: feeling\n",
      "wavy: performance\n",
      "spice: food\n",
      "flawlessly: feeling\n",
      "neck: target\n",
      "complaint: service\n",
      "drawback: service\n",
      "light: functionality\n",
      "extremely: feeling\n",
      "dissatisfied: feeling\n",
      "flimsy: quality\n",
      "delivery: service\n",
      "wonderful: feeling\n",
      "disappointed: feeling\n",
      "prompt: service\n",
      "receiver: target\n",
      "fascinating: appearance\n",
      "unusual: feeling\n",
      "silver: appearance\n",
      "weary: feeling\n",
      "surprisingly: feeling\n",
      "representative: service\n",
      "aesthetically: appearance\n",
      "vibrant: appearance\n",
      "accent: appearance\n",
      "multiple: size\n",
      "stylish: appearance\n",
      "instruction: service\n",
      "beautiful: appearance\n",
      "bronze: appearance\n",
      "fluorescent: functionality\n",
      "birthday: target\n",
      "boyfriend: target\n",
      "creamy: food\n",
      "call: service\n",
      "mother: target\n",
      "chocolate: food\n",
      "impressive: feeling\n",
      "language: service\n",
      "milk: food\n",
      "perfectly: feeling\n",
      "nightlight: functionality\n",
      "awesome: feeling\n",
      "neat: appearance\n",
      "led: functionality\n",
      "support: service\n",
      "contemporary: appearance\n",
      "shiny: appearance\n",
      "ton: size\n",
      "gray: appearance\n",
      "reply: service\n",
      "wobbly: quality\n",
      "very: feeling\n",
      "kid: target\n",
      "worried: feeling\n",
      "dimmable: functionality\n",
      "masculine: appearance\n",
      "illumination: functionality\n",
      "afterall: appearance\n",
      "terrific: feeling\n",
      "sturdy: quality\n",
      "wooden: appearance\n",
      "blue: appearance\n",
      "brightness: functionality\n",
      "black: appearance\n",
      "fabulous: feeling\n",
      "candelabra: appearance\n",
      "download: service\n",
      "useful: functionality\n",
      "beautifully: feeling\n",
      "adorable: appearance\n",
      "great: feeling\n",
      "slim: appearance\n",
      "grey: appearance\n",
      "happy: feeling\n",
      "industrial: appearance\n",
      "wide: size\n",
      "functional: appearance\n",
      "incredible: feeling\n",
      "several: size\n",
      "paint: appearance\n",
      "tall: size\n",
      "decorative: appearance\n",
      "beige: appearance\n",
      "child: target\n",
      "pleased: feeling\n",
      "odd: feeling\n",
      "downfall: service\n",
      "attractive: appearance\n",
      "wood: appearance\n",
      "wife: target\n",
      "message: service\n",
      "website: service\n",
      "mad: feeling\n",
      "table: target\n",
      "unhappy: feeling\n",
      "clamp: functionality\n",
      "intensity: functionality\n",
      "shabby: appearance\n",
      "link: service\n",
      "florescent: functionality\n",
      "fine: feeling\n",
      "adapter: functionality\n",
      "shipment: service\n",
      "snazzy: appearance\n",
      "elegant: appearance\n",
      "girl: target\n",
      "husband: target\n",
      "skeptical: feeling\n",
      "cute: appearance\n",
      "lighting: functionality\n",
      "nicely: feeling\n",
      "nervous: feeling\n",
      "color: appearance\n",
      "red: appearance\n",
      "macular: appearance\n",
      "credit: service\n",
      "awkward: feeling\n",
      "lot: size\n",
      "concern: service\n",
      "seller: service\n",
      "elderly: target\n",
      "handsome: appearance\n",
      "stable: quality\n",
      "desk: target\n",
      "matte: appearance\n",
      "yellow: appearance\n",
      "pole: functionality\n",
      "web: service\n",
      "surprised: feeling\n",
      "workbench: target\n",
      "gorgeous: appearance\n",
      "amazingly: feeling\n",
      "nose: target\n",
      "modern: appearance\n",
      "hubby: target\n",
      "glow: appearance\n",
      "dim: functionality\n",
      "disappointing: feeling\n",
      "shipper: service\n",
      "tacky: appearance\n",
      "minimalist: appearance\n",
      "purple: appearance\n",
      "narrow: size\n",
      "idiot: service\n",
      "downside: service\n",
      "anxious: feeling\n",
      "shade: appearance\n",
      "tricky: feeling\n",
      "clip: functionality\n",
      "bulky: size\n",
      "face: target\n",
      "feminine: appearance\n",
      "flexible: functionality\n",
      "college: target\n",
      "chic: appearance\n",
      "exceptional: feeling\n",
      "mom: target\n",
      "satisfied: feeling\n",
      "compact: appearance\n",
      "software: service\n",
      "son: target\n",
      "arm: functionality\n",
      "tilt: functionality\n",
      "daughter: target\n",
      "brass: appearance\n",
      "company: service\n",
      "yellowish: appearance\n",
      "bright: functionality\n",
      "unique: appearance\n",
      "lovely: appearance\n",
      "awful: feeling\n",
      "cold: performance\n",
      "afraid: feeling\n",
      "daylight: functionality\n",
      "answer: service\n",
      "impressed: feeling\n",
      "gloss: appearance\n",
      "excellent: feeling\n",
      "thrill: appearance\n",
      "sleek: appearance\n",
      "esthetician: service\n",
      "secure: quality\n",
      "fairly: feeling\n",
      "inherently: appearance\n",
      "sorry: feeling\n",
      "charge: functionality\n",
      "appearance: appearance\n",
      "irate: appearance\n",
      "colored: appearance\n",
      "magenta: appearance\n",
      "relatively: feeling\n",
      "difficult: feeling\n",
      "friend: target\n",
      "headboard: target\n",
      "hesitant: feeling\n",
      "glossy: appearance\n",
      "receipt: service\n",
      "amazing: feeling\n",
      "fantastic: feeling\n",
      "vintage: appearance\n",
      "neutral: appearance\n",
      "aesthetic: appearance\n",
      "convenient: functionality\n",
      "durable: quality\n",
      "tension: functionality\n",
      "decently: feeling\n",
      "large: size\n",
      "incandescent: functionality\n",
      "fond: feeling\n",
      "super: feeling\n",
      "classy: appearance\n"
     ]
    }
   ],
   "source": [
    "label.AutoLabeling('LampSet/IAC.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "208"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len([item for sublist in label.dictionary.values() for item in sublist])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label.Save('LampSet/dictionary.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis - 展示\n",
    "1. load_product(input_path) : input 產品資料夾路徑\n",
    "2. list_feature() : 特徵列表\n",
    "3. list_product() : 產品列表\n",
    "4. list_brand() : 品牌列表\n",
    "5. keyword(feature, 產品名稱or品牌名稱, flag) : flag = 0是產品名稱，1是品牌名稱\n",
    "6. set_time_interval(start, end) : 設定時間區隔input format (year, month, day)\n",
    "7. brand_compare(品牌名稱, 品牌名稱) : 兩品牌情緒分數的折線圖(時間間隔6個月)\n",
    "8. brand_overview() : 所有品牌，所有特徵整理成的dataframe\n",
    "9. all_brand_compare('sentiment'/'volume', feature) : 所有品牌在各feature的情緒分數或聲量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import Analyze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyze = Analyze.Analyze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "analyze.load_product('LampSet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 16/16 [00:00<00:00, 34.19it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze.all_brand_compare('volume', ['appearance', 'feeling', 'performance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['size', 'performance', 'appearance', 'quality', 'functionality', 'price', 'feeling', 'service', 'target']\n"
     ]
    }
   ],
   "source": [
    "analyze.list_feature()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyze.list_product()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creative Hobbies\n",
      "JINZO\n",
      "Lerro\n",
      "Normande Lighting\n",
      "Tenergy\n",
      "Elegant Designs\n",
      "Durapower\n",
      "MLGB\n",
      "Rustic State\n",
      "LIGHTACCENTS\n",
      "Park Madison Lighting\n",
      "Kenroy Home\n",
      "Amora Lighting\n",
      "Neatfi\n",
      "NACATIN\n",
      "OttLite\n"
     ]
    }
   ],
   "source": [
    "analyze.list_brand()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>tf</th>\n",
       "      <th>sent</th>\n",
       "      <th>not</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>light</td>\n",
       "      <td>7</td>\n",
       "      <td>0.295867</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bright</td>\n",
       "      <td>4</td>\n",
       "      <td>0.651450</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>dim</td>\n",
       "      <td>3</td>\n",
       "      <td>0.440400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>lighting</td>\n",
       "      <td>1</td>\n",
       "      <td>0.611400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>illumination</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>incandescent</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>nightlight</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            term  tf      sent  not\n",
       "13         light   7  0.295867    2\n",
       "6         bright   4  0.651450    1\n",
       "12           dim   3  0.440400    0\n",
       "38      lighting   1  0.611400    0\n",
       "42  illumination   1  0.000000    0\n",
       "7   incandescent   1  0.000000    0\n",
       "50    nightlight   1  0.000000    0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyze.keyword('functionality', 'JINZO', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyze.set_time_interval((2017,1,1), (2019,1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze.two_brand_compare('JINZO', 'Kenroy Home')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyze.brand_overview()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Labeling (Manual) - 手動標label\n",
    "1. CreatingMatrix(input_path) : input IAC 整理IAC間的相似性\n",
    "2. Labeling() : 手動標，標完會自動標剩下沒有分群到的\n",
    "3. Add(詞彙, feature) : 詞彙應為list，可以手動將一些詞彙新增進辭典\n",
    "4. Save(output_path) : 存檔"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Labeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "label = Labeling.Labeling()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1600/1600 [00:50<00:00, 31.46it/s]\n"
     ]
    }
   ],
   "source": [
    "label.CreatingMatrix('LampSet/IAC.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label.Labeling()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "label.Add(['small', 'large'], 'size')\n",
    "label.Add(['narrow'], 'size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "label.Save('LampSet/dictionary_manual.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "196"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len([item for sublist in label.dictionary.values() for item in sublist])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}