aspect_sa/sentiwordnet

Sentiwordnet.ipynb

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   "source": [
    "from nltk.corpus import wordnet\n",
    "\n",
    "def get_wordnet_pos(treebank_tag):\n",
    "\n",
    "    if treebank_tag.startswith('J'):\n",
    "        return wordnet.ADJ\n",
    "    elif treebank_tag.startswith('V'):\n",
    "        return wordnet.VERB\n",
    "    elif treebank_tag.startswith('N'):\n",
    "        return wordnet.NOUN\n",
    "    elif treebank_tag.startswith('R'):\n",
    "        return wordnet.ADV\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "positive_words = []\n",
    "negative_words = []\n",
    "\n",
    "for i in df['Dominant_topic'].unique():\n",
    "    if i == 1:\n",
    "        tmp_1 = df.loc[df['Dominant_topic'] == i]\n",
    "                \n",
    "        for j in tmp_1['tokenise'].values.tolist():\n",
    "            for p in nltk.pos_tag(j):\n",
    "                get_pos_tag = get_wordnet_pos(p[1])\n",
    "                if type(get_pos_tag) == str:\n",
    "                    try:        \n",
    "                        synset = swn.senti_synset(p[0] + '.' + get_pos_tag +'.01')\n",
    "\n",
    "                        if synset.obj_score() <= 0.49:\n",
    "                            if synset.pos_score() > synset.neg_score() and p[0] in wn_affect:\n",
    "                                    positive_words.append(p[0])\n",
    "                            elif synset.neg_score() > synset.pos_score() and p[0] in wn_affect:\n",
    "                                    negative_words.append(p[0])      \n",
    "                    except:\n",
    "                        pass"
   ]
  }
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