stemming als parameter und anpassungen für evaluation
This commit is contained in:
		| @ -147,34 +147,7 @@ | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 5, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "ename": "KeyError", | ||||
|      "evalue": "'character'", | ||||
|      "output_type": "error", | ||||
|      "traceback": [ | ||||
|       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | ||||
|       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)", | ||||
|       "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   2482\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2483\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mlibts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value_box\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2484\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | ||||
|       "\u001b[0;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.get_value_box (pandas/_libs/tslib.c:18843)\u001b[0;34m()\u001b[0m\n", | ||||
|       "\u001b[0;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.get_value_box (pandas/_libs/tslib.c:18477)\u001b[0;34m()\u001b[0m\n", | ||||
|       "\u001b[0;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer", | ||||
|       "\nDuring handling of the above exception, another exception occurred:\n", | ||||
|       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)", | ||||
|       "\u001b[0;32m<ipython-input-5-2e408a3beaf0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#print(sys.path)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mnaive_approach\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mclf_naive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", | ||||
|       "\u001b[0;32m~/GitRepos/NLP-LAB/Project/naive_approach/naive_approach.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0mtableDict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m     \u001b[0mtableDict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'character'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'description'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;31m#######################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | ||||
|       "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    599\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | ||||
|       "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   2489\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2490\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2491\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2492\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2493\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | ||||
|       "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   2475\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2476\u001b[0m             return self._engine.get_value(s, k,\n\u001b[0;32m-> 2477\u001b[0;31m                                           tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[1;32m   2478\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2479\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'integer'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | ||||
|       "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[0;34m()\u001b[0m\n", | ||||
|       "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[0;34m()\u001b[0m\n", | ||||
|       "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", | ||||
|       "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", | ||||
|       "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", | ||||
|       "\u001b[0;31mKeyError\u001b[0m: 'character'" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "outputs": [], | ||||
|    "source": [ | ||||
|     "#sys.path.append(\"..\")\n", | ||||
|     "#print(sys.path)\n", | ||||
| @ -184,13 +157,13 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": null, | ||||
|    "execution_count": 6, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
|    "outputs": [], | ||||
|    "source": [ | ||||
|     "tmp_dict = clf_naive.prepareData()" | ||||
|     "tmp_dict = clf_naive.prepareData(stem=True)" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
| @ -203,10 +176,8 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": null, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
|    "execution_count": 7, | ||||
|    "metadata": {}, | ||||
|    "outputs": [], | ||||
|    "source": [ | ||||
|     "def merged_prediction(msg , split = 0.5 , number = 8, target_emojis = top_emojis):\n", | ||||
| @ -216,7 +187,7 @@ | ||||
|     "    number_naive = round((1-split)*number)\n", | ||||
|     "    \n", | ||||
|     "    #predict emojis with the naive approach\n", | ||||
|     "    prediction_naive , prediction_naive_values = clf_naive.predict(sentence = msg, lookup= tmp_dict, n = number_naive, em)\n", | ||||
|     "    prediction_naive , prediction_naive_values = clf_naive.predict(sentence = msg, lookup= tmp_dict, n = number_naive, embeddings = \"wordnet\", stem = True)\n", | ||||
|     "\n", | ||||
|     "    #filter 0 values\n", | ||||
|     "    tmp1 = []\n", | ||||
| @ -256,7 +227,7 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": null, | ||||
|    "execution_count": 8, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
| @ -288,22 +259,89 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": null, | ||||
|    "execution_count": 9, | ||||
|    "metadata": {}, | ||||
|    "outputs": [], | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "data": { | ||||
|       "text/html": [ | ||||
|        "<div>\n", | ||||
|        "<style>\n", | ||||
|        "    .dataframe thead tr:only-child th {\n", | ||||
|        "        text-align: right;\n", | ||||
|        "    }\n", | ||||
|        "\n", | ||||
|        "    .dataframe thead th {\n", | ||||
|        "        text-align: left;\n", | ||||
|        "    }\n", | ||||
|        "\n", | ||||
|        "    .dataframe tbody tr th {\n", | ||||
|        "        vertical-align: top;\n", | ||||
|        "    }\n", | ||||
|        "</style>\n", | ||||
|        "<table border=\"1\" class=\"dataframe\">\n", | ||||
|        "  <thead>\n", | ||||
|        "    <tr style=\"text-align: right;\">\n", | ||||
|        "      <th></th>\n", | ||||
|        "      <th>Sentence</th>\n", | ||||
|        "      <th>prediction</th>\n", | ||||
|        "    </tr>\n", | ||||
|        "  </thead>\n", | ||||
|        "  <tbody>\n", | ||||
|        "    <tr>\n", | ||||
|        "      <th>0</th>\n", | ||||
|        "      <td>i like computer games</td>\n", | ||||
|        "      <td>NaN</td>\n", | ||||
|        "    </tr>\n", | ||||
|        "    <tr>\n", | ||||
|        "      <th>1</th>\n", | ||||
|        "      <td>it is great weather for using the swimming pool</td>\n", | ||||
|        "      <td>NaN</td>\n", | ||||
|        "    </tr>\n", | ||||
|        "    <tr>\n", | ||||
|        "      <th>2</th>\n", | ||||
|        "      <td>old cars are this loud</td>\n", | ||||
|        "      <td>NaN</td>\n", | ||||
|        "    </tr>\n", | ||||
|        "    <tr>\n", | ||||
|        "      <th>3</th>\n", | ||||
|        "      <td>i hear a plane above our house</td>\n", | ||||
|        "      <td>NaN</td>\n", | ||||
|        "    </tr>\n", | ||||
|        "    <tr>\n", | ||||
|        "      <th>4</th>\n", | ||||
|        "      <td>these are really cute pets</td>\n", | ||||
|        "      <td>NaN</td>\n", | ||||
|        "    </tr>\n", | ||||
|        "  </tbody>\n", | ||||
|        "</table>\n", | ||||
|        "</div>" | ||||
|       ], | ||||
|       "text/plain": [ | ||||
|        "                                          Sentence  prediction\n", | ||||
|        "0                            i like computer games         NaN\n", | ||||
|        "1  it is great weather for using the swimming pool         NaN\n", | ||||
|        "2                           old cars are this loud         NaN\n", | ||||
|        "3                   i hear a plane above our house         NaN\n", | ||||
|        "4                       these are really cute pets         NaN" | ||||
|       ] | ||||
|      }, | ||||
|      "execution_count": 9, | ||||
|      "metadata": {}, | ||||
|      "output_type": "execute_result" | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "# get table\n", | ||||
|     "import pandas as pd\n", | ||||
|     "df = pd.read_csv(\"Evaluation Sentences - Tabellenblatt1.csv\", sep=\"\\t\")\n", | ||||
|     "df = pd.read_csv(\"Evaluation Sentences - Topic related sentences.csv\")#, sep=\"\\t\")\n", | ||||
|     "df.head()" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": null, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
|    "execution_count": 10, | ||||
|    "metadata": {}, | ||||
|    "outputs": [], | ||||
|    "source": [ | ||||
|     "all_predictions = []\n", | ||||
| @ -325,9 +363,78 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": null, | ||||
|    "execution_count": 11, | ||||
|    "metadata": {}, | ||||
|    "outputs": [], | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "data": { | ||||
|       "text/html": [ | ||||
|        "<div>\n", | ||||
|        "<style>\n", | ||||
|        "    .dataframe thead tr:only-child th {\n", | ||||
|        "        text-align: right;\n", | ||||
|        "    }\n", | ||||
|        "\n", | ||||
|        "    .dataframe thead th {\n", | ||||
|        "        text-align: left;\n", | ||||
|        "    }\n", | ||||
|        "\n", | ||||
|        "    .dataframe tbody tr th {\n", | ||||
|        "        vertical-align: top;\n", | ||||
|        "    }\n", | ||||
|        "</style>\n", | ||||
|        "<table border=\"1\" class=\"dataframe\">\n", | ||||
|        "  <thead>\n", | ||||
|        "    <tr style=\"text-align: right;\">\n", | ||||
|        "      <th></th>\n", | ||||
|        "      <th>Sentence</th>\n", | ||||
|        "      <th>prediction</th>\n", | ||||
|        "    </tr>\n", | ||||
|        "  </thead>\n", | ||||
|        "  <tbody>\n", | ||||
|        "    <tr>\n", | ||||
|        "      <th>0</th>\n", | ||||
|        "      <td>i like computer games</td>\n", | ||||
|        "      <td>😅😂😢😭😁😌🎮🎲</td>\n", | ||||
|        "    </tr>\n", | ||||
|        "    <tr>\n", | ||||
|        "      <th>1</th>\n", | ||||
|        "      <td>it is great weather for using the swimming pool</td>\n", | ||||
|        "      <td>😌😁😎🙌😀😉🎐🍃</td>\n", | ||||
|        "    </tr>\n", | ||||
|        "    <tr>\n", | ||||
|        "      <th>2</th>\n", | ||||
|        "      <td>old cars are this loud</td>\n", | ||||
|        "      <td>😅😂😢😭🚕🚃🚚🚋</td>\n", | ||||
|        "    </tr>\n", | ||||
|        "    <tr>\n", | ||||
|        "      <th>3</th>\n", | ||||
|        "      <td>i hear a plane above our house</td>\n", | ||||
|        "      <td>😅😂😢😭😁😔😌😉</td>\n", | ||||
|        "    </tr>\n", | ||||
|        "    <tr>\n", | ||||
|        "      <th>4</th>\n", | ||||
|        "      <td>these are really cute pets</td>\n", | ||||
|        "      <td>😂😅😁😌😎😉🙌😀</td>\n", | ||||
|        "    </tr>\n", | ||||
|        "  </tbody>\n", | ||||
|        "</table>\n", | ||||
|        "</div>" | ||||
|       ], | ||||
|       "text/plain": [ | ||||
|        "                                          Sentence prediction\n", | ||||
|        "0                            i like computer games   😅😂😢😭😁😌🎮🎲\n", | ||||
|        "1  it is great weather for using the swimming pool   😌😁😎🙌😀😉🎐🍃\n", | ||||
|        "2                           old cars are this loud   😅😂😢😭🚕🚃🚚🚋\n", | ||||
|        "3                   i hear a plane above our house   😅😂😢😭😁😔😌😉\n", | ||||
|        "4                       these are really cute pets   😂😅😁😌😎😉🙌😀" | ||||
|       ] | ||||
|      }, | ||||
|      "execution_count": 11, | ||||
|      "metadata": {}, | ||||
|      "output_type": "execute_result" | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "df[\"prediction\"] = all_predictions\n", | ||||
|     "\n", | ||||
| @ -337,13 +444,13 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": null, | ||||
|    "execution_count": 12, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
|    "outputs": [], | ||||
|    "source": [ | ||||
|     "df.to_csv(\"Evaluation Sentences - Wordnet - newClf.csv\", sep='\\t', encoding='utf-8')" | ||||
|     "df.to_csv(\"E_S - topic - wordnet - stemming.csv\", sep='\\t', encoding='utf-8')" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|  | ||||
| @ -18,7 +18,7 @@ import pprint | ||||
| from gensim.models import Word2Vec, KeyedVectors | ||||
|  | ||||
| # # Naive Approach | ||||
| table = pd.read_csv('../Tools/emoji_descriptions_preprocessed.csv') | ||||
| table = pd.read_csv('../Tools/emoji_descriptions_preprocessed.csv', delimiter = ";") | ||||
|  | ||||
| ##Store table in the format: | ||||
| ## { index: [emoji, description]} | ||||
| @ -49,7 +49,7 @@ def evaluate_sentence(sentence, description_key = 'description', lang = 'eng', e | ||||
|     if embeddings=="word2Vec": | ||||
|         wv = KeyedVectors.load(str(__location__)+"/word2vec.model", mmap='r') | ||||
|     elif embeddings=="fastText": | ||||
|         wv = KeyedVectors.load("/fastTextVectors.kv", mmap='r') | ||||
|         wv = KeyedVectors.load(str(__location__)+"/fastTextVectors.kv", mmap='r') | ||||
|          | ||||
|     if (stem): | ||||
|         sentence = stemming(sentence) | ||||
| @ -118,9 +118,9 @@ def prepareData(stem=True, lower=True): | ||||
| # make a prediction for an input sentence | ||||
| # embeddings = ["wordnet", "word2Vec", "fastText"] | ||||
| def predict(sentence, lookup, emojis_to_consider="all", criteria="threshold", lang = 'eng',\ | ||||
|             embeddings="wordnet", n=10, t=0.9): | ||||
|             embeddings="wordnet", n=10, t=0.9, stem = True): | ||||
|  | ||||
|     result = evaluate_sentence(sentence, lang, emojis_to_consider=emojis_to_consider, embeddings=embeddings) | ||||
|     result = evaluate_sentence(sentence, lang, emojis_to_consider=emojis_to_consider, embeddings=embeddings, stem = stem) | ||||
|      | ||||
|     try: | ||||
|         if(criteria=="summed"): | ||||
|  | ||||
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