lstm-text-and-emoji.ipynb

{"metadata":{"_change_revision":0,"_is_fork":false,"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":799881,"sourceType":"datasetVersion","datasetId":16},{"sourceId":8138982,"sourceType":"datasetVersion","datasetId":4811775},{"sourceId":8306599,"sourceType":"datasetVersion","datasetId":4934266}],"dockerImageVersionId":30699,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Embedding, LSTM, SpatialDropout1D\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.utils import to_categorical\nimport re\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory","metadata":{"_cell_guid":"6c53202d-5c34-4859-e7e9-8ef5c7068287","_uuid":"717bb968c36b9325c7d4cae5724a3672e49ff243","execution":{"iopub.status.busy":"2024-05-04T01:48:45.071253Z","iopub.execute_input":"2024-05-04T01:48:45.071942Z","iopub.status.idle":"2024-05-04T01:48:58.239686Z","shell.execute_reply.started":"2024-05-04T01:48:45.071909Z","shell.execute_reply":"2024-05-04T01:48:58.238527Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"2024-05-04 01:48:48.677351: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-05-04 01:48:48.677436: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-05-04 01:48:48.800462: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"}]},{"cell_type":"code","source":"data = pd.read_csv('../input/final-sarcasm-dataset/Dataset.csv')\nprint(data.columns)\ndata = data[['Comments','Sarcasm']]\ndata.head()","metadata":{"_cell_guid":"89c8c923-c0bf-7b35-9ab8-e63f00b74e5a","_uuid":"d2bc3bbd2ea3961c49e6673145a0a7226c160e58","execution":{"iopub.status.busy":"2024-05-04T01:48:58.241477Z","iopub.execute_input":"2024-05-04T01:48:58.242047Z","iopub.status.idle":"2024-05-04T01:48:58.426772Z","shell.execute_reply.started":"2024-05-04T01:48:58.242020Z","shell.execute_reply":"2024-05-04T01:48:58.425862Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Index(['Comments', 'Emojis', 'Text', 'Sarcasm'], dtype='object')\n","output_type":"stream"},{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"                                            Comments  Sarcasm\n0   That's how we roll😎 . . . . . . . . #cinemadd...        1\n1                                       😂😂😂 #Sarcasm        1\n2   At my bro Barbara school . I'm only one can i...        1\n3   And I don’t know why people get hurt 😄 #sarca...        1\n4   Yay. I see a #cold in my near #future 😂 #joke...        1","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>Comments</th>\n      <th>Sarcasm</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>That's how we roll😎 . . . . . . . . #cinemadd...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>😂😂😂 #Sarcasm</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>At my bro Barbara school . I'm only one can i...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>And I don’t know why people get hurt 😄 #sarca...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Yay. I see a #cold in my near #future 😂 #joke...</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data['Comments'] = data['Comments'].apply(lambda x: x.lower())\n\n# Function to add space after every alphanumeric sequence\ndef add_space_after_alphanumeric(text):\n    return re.sub(r'(\\w+)', r'\\1 ', text)\n\n# Apply the function to the 'Comments' column\ndata['Comments'] = data['Comments'].apply(add_space_after_alphanumeric)\n\n# data['Emojis'] = data['Emojis'].astype(str)\n\nprint(data[ data['Sarcasm'] == 1].size)\nprint(data[ data['Sarcasm'] == 0].size)\n\ndata.head()","metadata":{"_cell_guid":"43632d2d-6160-12ce-48b0-e5eb1c207076","_uuid":"d0f8b4542106a279f7398db7285ae5e370b2e813","execution":{"iopub.status.busy":"2024-05-04T01:49:52.236831Z","iopub.execute_input":"2024-05-04T01:49:52.237910Z","iopub.status.idle":"2024-05-04T01:49:53.098022Z","shell.execute_reply.started":"2024-05-04T01:49:52.237865Z","shell.execute_reply":"2024-05-04T01:49:53.097002Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"18520\n26140\n","output_type":"stream"},{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"                                            Comments  Sarcasm\n0   that  's   how   we   roll  😎 . . . . . . . ....        1\n1                                     😂😂😂 #sarcasm          1\n2   at   my   bro   barbara   school   . i  'm   ...        1\n3   and   i   don  ’t   know   why   people   get...        1\n4   yay  . i   see   a   #cold   in   my   near  ...        1","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>Comments</th>\n      <th>Sarcasm</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>that  's   how   we   roll  😎 . . . . . . . ....</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>😂😂😂 #sarcasm</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>at   my   bro   barbara   school   . i  'm   ...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>and   i   don  ’t   know   why   people   get...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>yay  . i   see   a   #cold   in   my   near  ...</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"max_features = 2000\ntokenizer = Tokenizer(num_words=max_features, split=' .,?/-!#&')\ntokenizer.fit_on_texts(data['Comments'].values)\nX = tokenizer.texts_to_sequences(data['Comments'].values)\nX = pad_sequences(X)","metadata":{"execution":{"iopub.status.busy":"2024-05-04T01:50:05.713545Z","iopub.execute_input":"2024-05-04T01:50:05.714369Z","iopub.status.idle":"2024-05-04T01:50:06.782131Z","shell.execute_reply.started":"2024-05-04T01:50:05.714335Z","shell.execute_reply":"2024-05-04T01:50:06.781113Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"embed_dim = 128\nlstm_out = 196\n\nmodel = Sequential()\nmodel.add(Embedding(max_features, embed_dim,input_length = X.shape[1]))\nmodel.add(SpatialDropout1D(0.4))\nmodel.add(LSTM(lstm_out, dropout=0.2, recurrent_dropout=0.2))\nmodel.add(Dense(2,activation='softmax'))\nmodel.compile(loss = 'categorical_crossentropy', optimizer='adam',metrics = ['accuracy'])\nmodel.build((None, X.shape[1]))\nprint(model.summary())","metadata":{"_cell_guid":"1ba3cf60-a83c-9c21-05e0-b14303027e93","_uuid":"05cb9ef0ec9e0a4067e3ab7c1bda7b2c1211feda","execution":{"iopub.status.busy":"2024-05-04T01:50:07.513658Z","iopub.execute_input":"2024-05-04T01:50:07.514449Z","iopub.status.idle":"2024-05-04T01:50:08.787047Z","shell.execute_reply.started":"2024-05-04T01:50:07.514416Z","shell.execute_reply":"2024-05-04T01:50:08.786219Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/keras/src/layers/core/embedding.py:86: UserWarning: Argument `input_length` is deprecated. Just remove it.\n  warnings.warn(\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential\"\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m37\u001b[0m, \u001b[38;5;34m128\u001b[0m)        │       \u001b[38;5;34m256,000\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ spatial_dropout1d               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m37\u001b[0m, \u001b[38;5;34m128\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n│ (\u001b[38;5;33mSpatialDropout1D\u001b[0m)              │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m196\u001b[0m)            │       \u001b[38;5;34m254,800\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)              │           \u001b[38;5;34m394\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">37</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │       <span style=\"color: #00af00; text-decoration-color: #00af00\">256,000</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ spatial_dropout1d               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">37</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SpatialDropout1D</span>)              │                        │               │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">196</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">254,800</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">394</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m511,194\u001b[0m (1.95 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">511,194</span> (1.95 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m511,194\u001b[0m (1.95 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">511,194</span> (1.95 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n</pre>\n"},"metadata":{}},{"name":"stdout","text":"None\n","output_type":"stream"}]},{"cell_type":"code","source":"Y = pd.get_dummies(data['Sarcasm']).values\nX_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.33, random_state = 42)\nprint(X_train.shape,Y_train.shape)\nprint(X_test.shape,Y_test.shape)","metadata":{"_cell_guid":"b35748b8-2353-3db2-e571-5fd22bb93eb0","_uuid":"a380bbfae2d098d407b138fc44622c9913a31c07","execution":{"iopub.status.busy":"2024-05-04T01:50:12.343772Z","iopub.execute_input":"2024-05-04T01:50:12.344130Z","iopub.status.idle":"2024-05-04T01:50:12.361403Z","shell.execute_reply.started":"2024-05-04T01:50:12.344101Z","shell.execute_reply":"2024-05-04T01:50:12.360506Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"(14961, 37) (14961, 2)\n(7369, 37) (7369, 2)\n","output_type":"stream"}]},{"cell_type":"code","source":"batch_size = 32\nmodel.fit(X_train, Y_train, epochs = 20, batch_size=batch_size, verbose = 2)","metadata":{"_cell_guid":"d5e499ac-2eba-6ff7-8d9a-ff65eb04099b","_uuid":"d0b239912cf67294a9f5af6883bb159c44318fc7","execution":{"iopub.status.busy":"2024-05-04T01:50:13.749974Z","iopub.execute_input":"2024-05-04T01:50:13.750343Z","iopub.status.idle":"2024-05-04T01:58:26.033479Z","shell.execute_reply.started":"2024-05-04T01:50:13.750314Z","shell.execute_reply":"2024-05-04T01:58:26.032523Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"Epoch 1/20\n468/468 - 27s - 57ms/step - accuracy: 0.8265 - loss: 0.4216\nEpoch 2/20\n468/468 - 23s - 49ms/step - accuracy: 0.8493 - loss: 0.3836\nEpoch 3/20\n468/468 - 23s - 49ms/step - accuracy: 0.8508 - loss: 0.3760\nEpoch 4/20\n468/468 - 23s - 49ms/step - accuracy: 0.8509 - loss: 0.3722\nEpoch 5/20\n468/468 - 22s - 48ms/step - accuracy: 0.8515 - loss: 0.3718\nEpoch 6/20\n468/468 - 23s - 48ms/step - accuracy: 0.8513 - loss: 0.3701\nEpoch 7/20\n468/468 - 22s - 48ms/step - accuracy: 0.8513 - loss: 0.3691\nEpoch 8/20\n468/468 - 23s - 48ms/step - accuracy: 0.8514 - loss: 0.3685\nEpoch 9/20\n468/468 - 22s - 47ms/step - accuracy: 0.8521 - loss: 0.3685\nEpoch 10/20\n468/468 - 22s - 48ms/step - accuracy: 0.8522 - loss: 0.3673\nEpoch 11/20\n468/468 - 23s - 49ms/step - accuracy: 0.8530 - loss: 0.3659\nEpoch 12/20\n468/468 - 41s - 87ms/step - accuracy: 0.8525 - loss: 0.3665\nEpoch 13/20\n468/468 - 23s - 49ms/step - accuracy: 0.8530 - loss: 0.3658\nEpoch 14/20\n468/468 - 22s - 48ms/step - accuracy: 0.8534 - loss: 0.3644\nEpoch 15/20\n468/468 - 23s - 49ms/step - accuracy: 0.8532 - loss: 0.3654\nEpoch 16/20\n468/468 - 23s - 49ms/step - accuracy: 0.8532 - loss: 0.3643\nEpoch 17/20\n468/468 - 23s - 48ms/step - accuracy: 0.8534 - loss: 0.3651\nEpoch 18/20\n468/468 - 22s - 47ms/step - accuracy: 0.8535 - loss: 0.3645\nEpoch 19/20\n468/468 - 23s - 49ms/step - accuracy: 0.8536 - loss: 0.3631\nEpoch 20/20\n468/468 - 41s - 87ms/step - accuracy: 0.8533 - loss: 0.3634\n","output_type":"stream"},{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.history.History at 0x7dab9650eda0>"},"metadata":{}}]},{"cell_type":"code","source":"model.save(\"Models/LSTM_text_and_emoji.h5\")","metadata":{"execution":{"iopub.status.busy":"2024-05-04T01:59:52.274082Z","iopub.execute_input":"2024-05-04T01:59:52.274458Z","iopub.status.idle":"2024-05-04T01:59:52.317779Z","shell.execute_reply.started":"2024-05-04T01:59:52.274426Z","shell.execute_reply":"2024-05-04T01:59:52.316851Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"validation_size = 1500\n\nX_validate = X_test[-validation_size:]\nY_validate = Y_test[-validation_size:]\nX_test = X_test[:-validation_size]\nY_test = Y_test[:-validation_size]\nscore,acc = model.evaluate(X_test, Y_test, verbose = 2, batch_size = batch_size)\nprint(\"score: %.2f\" % (score))\nprint(\"acc: %.2f\" % (acc))","metadata":{"_cell_guid":"a970f412-722f-6d6d-72c8-325d0901ccef","_uuid":"7872f6ea819a5d4d08394ba6db8436f9cb2cfe1c","execution":{"iopub.status.busy":"2024-05-04T01:59:55.908979Z","iopub.execute_input":"2024-05-04T01:59:55.909377Z","iopub.status.idle":"2024-05-04T01:59:58.904105Z","shell.execute_reply.started":"2024-05-04T01:59:55.909345Z","shell.execute_reply":"2024-05-04T01:59:58.903253Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"184/184 - 3s - 16ms/step - accuracy: 0.8559 - loss: 0.3686\nscore: 0.37\nacc: 0.86\n","output_type":"stream"}]},{"cell_type":"code","source":"pos_cnt, neg_cnt, pos_correct, neg_correct = 0, 0, 0, 0\nfor x in range(len(X_validate)):\n    \n    result = model.predict(X_validate[x].reshape(1,X_test.shape[1]),batch_size=1,verbose=0)[0]\n   \n    if np.argmax(result) == np.argmax(Y_validate[x]):\n        if np.argmax(Y_validate[x]) == 0:\n            neg_correct += 1\n        else:\n            pos_correct += 1\n       \n    if np.argmax(Y_validate[x]) == 0:\n        neg_cnt += 1\n    else:\n        pos_cnt += 1\n\n\n\nprint(\"pos_acc\", pos_correct/pos_cnt*100, \"%\")\nprint(\"neg_acc\", neg_correct/neg_cnt*100, \"%\")","metadata":{"_cell_guid":"1add73e9-c6fb-7e4c-8715-ea92f519d2a6","_uuid":"f80e9f3cf281adb3ab0357cbf6f886eb1dce3005","execution":{"iopub.status.busy":"2024-05-04T01:59:58.905429Z","iopub.execute_input":"2024-05-04T01:59:58.905728Z","iopub.status.idle":"2024-05-04T02:01:32.776226Z","shell.execute_reply.started":"2024-05-04T01:59:58.905704Z","shell.execute_reply":"2024-05-04T02:01:32.775228Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"pos_acc 67.98679867986799 %\nneg_acc 99.10514541387025 %\n","output_type":"stream"}]},{"cell_type":"code","source":"twt = ['Meetings: Because none of us is as dumb as all of us.']\n#vectorizing the tweet by the pre-fitted tokenizer instance\ntwt = tokenizer.texts_to_sequences(twt)\n#padding the tweet to have exactly the same shape as `embedding_2` input\ntwt = pad_sequences(twt, maxlen=28, dtype='int32', value=0)\nprint(twt)\nsentiment = model.predict(twt,batch_size=1,verbose = 2)[0]\nif(np.argmax(sentiment) == 0):\n    print(\"negative\")\nelif (np.argmax(sentiment) == 1):\n    print(\"positive\")","metadata":{"_cell_guid":"24c64f46-edd1-8d0b-7c7c-ef50fd26b2fd","_uuid":"d9aac68e2013b3beffb6a764cc5b85be83073e66","execution":{"iopub.status.busy":"2024-05-04T01:22:40.475904Z","iopub.execute_input":"2024-05-04T01:22:40.476544Z","iopub.status.idle":"2024-05-04T01:22:40.724663Z","shell.execute_reply.started":"2024-05-04T01:22:40.476509Z","shell.execute_reply":"2024-05-04T01:22:40.723765Z"},"trusted":true},"execution_count":69,"outputs":[{"name":"stdout","text":"[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n1/1 - 0s - 204ms/step\nnegative\n","output_type":"stream"}]}]}