Rule Based Turkish Language Sentiment Analysis.ipynb


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   "source": [
    "# A rule-based sentiment analysis\n",
    "#--> for more visit: https://medium.com/analytics-vidhya/rule-based-sentiment-analysis-with-python-for-turkeys-stock-market-839f85d7daaf"
   ]
  },
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   "execution_count": 1,
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   "source": [
    "import plotly.express as px #visualization\n",
    "import pandas as pd\n",
    "from nltk import word_tokenize\n",
    "import preprocessor as p\n",
    "from nltk.corpus import stopwords\n",
    "import numpy as np\n",
    "import re\n",
    "import nltk\n",
    "import string\n",
    "from evds import evdsAPI\n",
    "import preprocessor as p\n",
    "from snowballstemmer import TurkishStemmer\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "evds = evdsAPI('#########')\n",
    "pd.set_option('display.max_colwidth', -1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
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   "outputs": [
    {
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     "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 13440 entries, 0 to 13439\nData columns (total 4 columns):\ntext1        13440 non-null object\ntimestamp    13440 non-null datetime64[ns]\ndate         13440 non-null object\ntext         13440 non-null object\ndtypes: datetime64[ns](1), object(3)\nmemory usage: 420.1+ KB\n"
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   "source": [
    "df=pd.read_json('/Users/umar/Desktop/bist100twits.json')\n",
    "df['text1']=[p.clean(e) for e in df['text']]\n",
    "df=df[['text1',  'timestamp']]\n",
    "df['date']=df['timestamp'].dt.date\n",
    "df['text']=df['text1'].str.lower()\n",
    "df.info()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
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    {
     "output_type": "execute_result",
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      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1390d2128>"
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     "metadata": {},
     "execution_count": 6
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   ],
   "source": [
    "df.groupby('date')['text'].count().plot(kind=\"line\")"
   ]
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[['text1',  'timestamp']]\n",
    "df['date']=df['timestamp'].dt.date\n",
    "df['text']=df['text1'].str.lower()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "neg=pd.read_csv('/Users/umar/Desktop/negative_words_tr.txt')\n",
    "pos=pd.read_csv('/Users/umar/Desktop/positive_words_tr.txt')\n",
    "PUNCT_TO_REMOVE = string.punctuation\n",
    "stoplist=stopwords.words('turkish')\n",
    "def remove_punctuation(text):# notktalama işaretleri\n",
    "    \"\"\"custom function to remove the punctuation\"\"\"\n",
    "    return text.translate(str.maketrans('', '', PUNCT_TO_REMOVE))\n",
    "\n",
    "df['text'] = df['text'].apply(lambda text: remove_punctuation(text))\n",
    "def remove_stopwords(text):\n",
    "    \"\"\"custom function to remove the stopwords\"\"\"\n",
    "    return \" \".join([word for word in str(text).split() if word not in stoplist])\n",
    "\n",
    "df['text_stop'] = df['text'].apply(lambda text: remove_stopwords(text))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stemming_tokenizer(text): \n",
    "    stemmer = TurkishStemmer()\n",
    "    return [stemmer.stemWord(w) for w in word_tokenize(text)]\n",
    "df['text_token'] = df['text_stop'].apply(lambda text: stemming_tokenizer(text))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def word_finder1(x):\n",
    "    df_words = set(\" \".join(x).split(\" \"))\n",
    "    extract_words =  set(neg.eski.to_list()).intersection(df_words)\n",
    "    return ', '.join(extract_words)\n",
    "def word_finder2(x):\n",
    "    df_words = set(\" \".join(x).split(\" \"))\n",
    "    extract_words =  set(pos.olan.to_list()).intersection(df_words)\n",
    "    return ', '.join(extract_words)\n",
    "df['neg'] = df.text_token.apply(word_finder1)\n",
    "df['pos'] = df.text_token.apply(word_finder2)"
   ]
  },
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      "text/plain": "                                                                                                                                                                                                                                   text_token  \\\n4613  [y, bir, taraf, gidecek, ol, ora, …]                                                                                                                                                                                                      \n6615  [bist30, 389, bist100, 393, fai̇z, 000]                                                                                                                                                                                                   \n3968  [kiş, i̇talya, türki, ’, yi, virüs, sonuçlarıhat, öl, oran, aynı, ora, kıyasladık, görüyoruzkriz, beslene, kes, bellik, nakitteky, düşünenyol, göstere, parmak, say, kadarbu, mubarek, gece, kyi̇ç, istiyor, allah, kat, on, nasip, et]   \n\n        neg pos  \n4613             \n6615             \n3968  virüs      ",
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   "source": [
    "df[['text_token', 'neg', 'pos']].sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
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   "source": [
    "df['len_pos'] = df.pos.apply(lambda x: len(x.split(', ')))\n",
    "df['len_neg'] = df.neg.apply(lambda x: len(x.split(', ')))"
   ]
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   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
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   "source": [
    "df['len_neg']=df['len_neg']-1\n",
    "df['len_pos']=df['len_pos']-1"
   ]
  },
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   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
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   "source": [
    "df['text_count'] = df['text_stop'].str.split().apply(len).value_counts()\n",
    "df['polarity'] = df['len_pos'] - df['len_neg']\n",
    "df['sent_rate']=(df['len_pos'] + df['len_neg'])/ df['text_count']"
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    "d=pd.DataFrame(df.groupby('date')['polarity'].mean())\n",
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   ],
   "source": [
    "bist=evds.get_data(['TP.MK.F.BILESIK'] , startdate=\"01-03-2020\", enddate='01-05-2020')\n",
    "bist.columns=['tarih', 'kapanış']\n",
    "fig = px.line(bist, x=bist.tarih, y=bist.kapanış, title='BIST100 Index in the same date range')\n",
    "fig.show()"
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  }
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