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GUI.py

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+from PyQt5.QtWidgets import QApplication, QWidget, QLabel, QLineEdit, QVBoxLayout, QPushButton
+from PyQt5.QtGui import QFont
+from PyQt5.QtWidgets import QApplication, QWidget, QLabel, QLineEdit, QVBoxLayout, QPushButton, QComboBox
+import tensorflow as tf
+import numpy as np
+import tensorflow_hub as hub
+import tensorflow_text as text
+
+print ("Loading Models.....")
+Model2=tf.keras.models.load_model("./Bert_uncased_model_Tiwtter.h5",custom_objects={'KerasLayer':hub.KerasLayer})
+
+Model1=tf.keras.models.load_model("./Bert_uncased_model_Reddit.h5",custom_objects={'KerasLayer':hub.KerasLayer})
+classes=['Neutral', 'Positive', 'Negative']
+
+def show_message():
+    message = input_box.text()
+    selected_option = dropdown.currentIndex()
+    Model=Model2
+    if (selected_option==1):
+        Model=Model1
+    ans=Model.predict([message])
+    i=np.argmax(ans)
+    catagorie=classes[i]
+    percentage=str(int(ans[0][i]*100))+" %"
+    show_text=catagorie+" - "+percentage
+    label.setText(show_text)
+
+app = QApplication([])
+window = QWidget()
+window.setWindowTitle("Sentiment Analysis")
+window.setFixedSize(600, 300)  # Set a fixed window size
+
+layout = QVBoxLayout()
+
+label1 = QLabel("Enter a text:")
+label1.setFont(QFont("Arial", 14))  # Increase the font size
+layout.addWidget(label1)
+
+dropdown = QComboBox()
+dropdown.addItem("Bert uncased model Tiwtter (Model 2)")
+dropdown.addItem("Bert uncased model Reddit (Model 1)")
+dropdown.setFont(QFont("Arial", 12))  # Increase the font size
+layout.addWidget(dropdown)
+
+input_box = QLineEdit()
+input_box.setFont(QFont("Arial", 14))  # Increase the font size
+layout.addWidget(input_box)
+
+button = QPushButton("Classify")
+button.clicked.connect(show_message)
+button.setFont(QFont("Arial", 14))  # Increase the font size
+layout.addWidget(button)
+
+layout.addStretch()
+
+label = QLabel("")
+label.setFont(QFont("Arial", 12))  # Increase the font size
+layout.addWidget(label)
+label.setContentsMargins(150, 0, 250, 0)  # Add 15-pixel padding
+
+
+layout.addStretch()  # Add a stretchable space at the end to center-align the widgets
+
+window.setLayout(layout)
+window.show()
+app.exec_()

README.md

@@ -1,3 +1,56 @@
----
-license: apache-2.0
----
+
+#  Sentiment Analysis with GUI
+
+A Deep Learning Model which used for Sentiment analysis. The Accuracy it reach upto 85%. It train on 25000 text data. 
+
+# Neural Network Info
+The bert layer is integrated in the neural network at the second layer after input layer. The 3 GRU layer is for feature extraction
+then a Conv1D Layer is use after that making the output flatten and passing through a bunch of dense layer.
+
+
+## Info
+
+1) "Bert_uncased_model_Tiwtter.h5" has reached to the accuracy upto 85% just on 30 epochs. Loss it got is 0.51. This model is purly train in Twitter dataset.
+![SS1](https://github.com/somnathdashs/Sentiment-Analysis/blob/main/SS/Screenshot%202023-07-06%20215459.png?raw=true)
+
+2) "Bert_uncased_model_Reddit.h5" has reached to the accuracy upto 84% just on 35 epochs. Loss it got is 0.81. This model is not purly train in Twitter dataset but a bit of reddit's dataset is also used.
+![SS1](https://github.com/somnathdashs/Sentiment-Analysis/blob/main/SS/Screenshot%202023-07-06%20212910.png?raw=true)
+
+Loss :- Sparse_categorical_crossentropy
+
+Activatiion on last layer :- softmax
+
+Note: Max input length is 768 words. 
+
+
+## Screenshots
+
+![Preview](https://github.com/somnathdashs/Sentiment-Analysis/blob/main/SS/Screenshot%202023-07-09%20103349.png?raw=true)
+
+![Preview](https://github.com/somnathdashs/Sentiment-Analysis/blob/main/SS/Screenshot%202023-07-09%20103417.png?raw=true)
+
+![Preview](https://github.com/somnathdashs/Sentiment-Analysis/blob/main/SS/Screenshot%202023-07-09%20103425.png?raw=true)
+
+![Preview](https://github.com/somnathdashs/Sentiment-Analysis/blob/main/SS/Screenshot%202023-07-09%20103455.png?raw=true)
+
+![Preview](https://github.com/somnathdashs/Sentiment-Analysis/blob/main/SS/Screenshot%202023-07-09%20104740.png?raw=true)
+
+
+## Libray Used
+
+ ##### > opencv
+ ##### > tensorflow
+ ##### > numpy 
+ ##### > pickle 
+ ##### > bert
+ ##### > tensorflow_hub
+ ##### > tensorflow_text
+
+
+
+
+
+## Authors
+
+- [@Somnath Dash](https://www.github.com/somnathdashs)
+

main.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "import numpy as np,sklearn,os,cv2,pandas\n",
+    "import tensorflow_hub as hub\n",
+    "import tensorflow_text as text\n",
+    "from tensorflow.keras import Model,layers\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "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>Unnamed: 0</th>\n",
+       "      <th>Datetime</th>\n",
+       "      <th>Tweet Id</th>\n",
+       "      <th>Text</th>\n",
+       "      <th>Username</th>\n",
+       "      <th>sentiment</th>\n",
+       "      <th>sentiment_score</th>\n",
+       "      <th>emotion</th>\n",
+       "      <th>emotion_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2022-09-30 23:29:15+00:00</td>\n",
+       "      <td>1575991191170342912</td>\n",
+       "      <td>@Logitech @apple @Google @Microsoft @Dell @Len...</td>\n",
+       "      <td>ManjuSreedaran</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>0.853283</td>\n",
+       "      <td>anticipation</td>\n",
+       "      <td>0.587121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2022-09-30 21:46:35+00:00</td>\n",
+       "      <td>1575965354425131008</td>\n",
+       "      <td>@MK_habit_addict @official_stier @MortalKombat...</td>\n",
+       "      <td>MiKeMcDnet</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>0.519470</td>\n",
+       "      <td>joy</td>\n",
+       "      <td>0.886913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2022-09-30 21:18:02+00:00</td>\n",
+       "      <td>1575958171423752203</td>\n",
+       "      <td>As @CRN celebrates its 40th anniversary, Bob F...</td>\n",
+       "      <td>jfollett</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>0.763791</td>\n",
+       "      <td>joy</td>\n",
+       "      <td>0.960347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2022-09-30 20:05:24+00:00</td>\n",
+       "      <td>1575939891485032450</td>\n",
+       "      <td>@dell your customer service is horrible especi...</td>\n",
+       "      <td>daveccarr</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>0.954023</td>\n",
+       "      <td>anger</td>\n",
+       "      <td>0.983203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2022-09-30 20:03:17+00:00</td>\n",
+       "      <td>1575939359160750080</td>\n",
+       "      <td>@zacokalo @Dell @DellCares @Dell give the man ...</td>\n",
+       "      <td>heycamella</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>0.529170</td>\n",
+       "      <td>anger</td>\n",
+       "      <td>0.776124</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0                   Datetime             Tweet Id  \\\n",
+       "0           0  2022-09-30 23:29:15+00:00  1575991191170342912   \n",
+       "1           1  2022-09-30 21:46:35+00:00  1575965354425131008   \n",
+       "2           2  2022-09-30 21:18:02+00:00  1575958171423752203   \n",
+       "3           3  2022-09-30 20:05:24+00:00  1575939891485032450   \n",
+       "4           4  2022-09-30 20:03:17+00:00  1575939359160750080   \n",
+       "\n",
+       "                                                Text        Username  \\\n",
+       "0  @Logitech @apple @Google @Microsoft @Dell @Len...  ManjuSreedaran   \n",
+       "1  @MK_habit_addict @official_stier @MortalKombat...      MiKeMcDnet   \n",
+       "2  As @CRN celebrates its 40th anniversary, Bob F...        jfollett   \n",
+       "3  @dell your customer service is horrible especi...       daveccarr   \n",
+       "4  @zacokalo @Dell @DellCares @Dell give the man ...      heycamella   \n",
+       "\n",
+       "  sentiment  sentiment_score       emotion  emotion_score  \n",
+       "0   neutral         0.853283  anticipation       0.587121  \n",
+       "1   neutral         0.519470           joy       0.886913  \n",
+       "2  positive         0.763791           joy       0.960347  \n",
+       "3  negative         0.954023         anger       0.983203  \n",
+       "4   neutral         0.529170         anger       0.776124  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df=pandas.read_csv(\"./sentiment-emotion-labelled_Dell_tweets.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['neutral', 'positive', 'negative'], dtype=object)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X,Y=df.Text,df.sentiment\n",
+    "Y.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Classes=['neutral', 'positive', 'negative']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((24970,), (24970,))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.shape,Y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X=np.array(X)\n",
+    "Y=np.array(Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bert_preprocess = hub.KerasLayer(\n",
+    "    \"https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3\")\n",
+    "bert_encoder = hub.KerasLayer(\n",
+    "    \"https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((24970,), (24970,))"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "New_Y=[]\n",
+    "for i,j in enumerate(Y):\n",
+    "    label=-2\n",
+    "    for k,l in enumerate(Classes):\n",
+    "        if l==j:\n",
+    "            label=k\n",
+    "            New_Y.append(label)\n",
+    "New_Y=np.array(New_Y)\n",
+    "New_Y.shape,X.shape\n",
+    "# 0 => Neutral and 1 => positive and 2 => negative"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split as tts\n",
+    "X_train, X_test, y_train, y_test = tts(X,New_Y,test_size=0.2, random_state=42,shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((19976,), (4994,), (19976,), (4994,))"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape,X_test.shape,y_train.shape,y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(\"@Dell Apparently your company doesn't think that a swollen battery on a laptop that isn't even a year old is an issue. You are not honoring your warranty and overall putting me in the position where I am a walking fire hazard which as a customer for years now isn't right.\",\n",
+       " 2)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train[0],y_train[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'bert_preprocess' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_12248\\1158974034.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mtextinput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mInput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"text\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mpre\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbert_preprocess\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtextinput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbert_encoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpre\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# NN Layer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mpo\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mend\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"sequence_output\"\u001b[0m\u001b[1;33m]\u001b[0m  \u001b[1;31m#[\"pooled_output\"]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'bert_preprocess' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "textinput=layers.Input(shape=(),dtype=tf.string,name=\"text\")\n",
+    "pre=bert_preprocess(textinput)\n",
+    "end=bert_encoder(pre)\n",
+    "# NN Layer\n",
+    "po=end[\"sequence_output\"]  #[\"pooled_output\"]\n",
+    "l=layers.Bidirectional(layers.GRU(128, return_sequences=True))(po)\n",
+    "l=layers.Bidirectional(layers.GRU(64, return_sequences=True))(l)\n",
+    "l=layers.Conv1D(64, 1, activation=\"relu\", padding='same')(l)\n",
+    "l=layers.Flatten()(l)\n",
+    "l=layers.Dropout(0.2)(l)\n",
+    "l=layers.Dense(128,activation=\"relu\",name=\"input\")(l)\n",
+    "l=layers.Dense(64,activation=\"relu\",name=\"in\")(l)\n",
+    "l=layers.Dense(16,activation=\"relu\",name=\"in1\")(l)\n",
+    "l=layers.Dropout(0.2)(l)\n",
+    "l=layers.Dense(3,activation=\"softmax\",name=\"output\")(l) #[0.2,0.3,0.5] = 1\n",
+    "Model=tf.keras.Model(inputs=textinput,outputs=[l])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Model.compile(\"adam\",loss=\"sparse_categorical_crossentropy\",metrics=[\"accuracy\"])\n",
+    "Model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hist=Model.fit(X_train,y_train,epochs=30,batch_size=80)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Model.evaluate(X_test,y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Model.save(\"./Model_3_Bert_Uncase.h5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A=Model.predict([\"you awake from a deep trance, step away from the phone to see your friends & family\"])\n",
+    "a=np.argmax(A)\n",
+    "Classes[a],int(A[0][a]*100)"
+   ]
+  }
+ ],
+ "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": "3.7.2"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}