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import pandas as pd
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import torch
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import numpy
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import torch.nn as nn
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import torch.optim as optim
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from sklearn.feature_extraction.text import CountVectorizer
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data = {
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"text": [
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"This movie was great",
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"I did not like this movie",
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"The acting was terrible",
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"I loved the plot",
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"It was a boring experience",
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"What a fantastic film!",
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"I hated it",
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"It was okay",
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"Absolutely wonderful!",
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"Not my favorite"
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"Was very good"
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"Very good"
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],
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"label": [
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1,
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0,
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0,
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1,
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0,
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1,
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0,
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0,
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1,
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0,
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1,
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1
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]
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}
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df = pd.DataFrame(data)
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df.to_csv("data.csv", index=False)
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df = pd.read_csv("data.csv")
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vectorizer = CountVectorizer()
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X = vectorizer.fit_transform(df["text"]).toarray()
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y = df["label"].values
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X_tensor = torch.tensor(X, dtype=torch.float32)
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y_tensor = torch.tensor(y, dtype=torch.float32).view(-1, 1)
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class SentimentAnalysisModel(nn.Module):
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def __init__(self, input_size):
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super(SentimentAnalysisModel, self).__init__()
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self.fc1 = nn.Linear(input_size, 8)
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self.fc2 = nn.Linear(8, 1)
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self.relu = nn.ReLU()
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def forward(self, x):
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x = self.relu(self.fc1(x))
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x = torch.sigmoid(self.fc2(x))
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return x
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input_size = X.shape[1]
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model = SentimentAnalysisModel(input_size)
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criterion = nn.BCELoss()
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optimizer = optim.Adam(model.parameters(), lr=0.01)
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epochs = 100
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for epoch in range(epochs):
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y_pred = model(X_tensor)
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loss = criterion(y_pred, y_tensor)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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if (epoch+1) % 10 == 0:
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print(f"Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}")
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def predict_sentiment(text):
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text_vectorized = vectorizer.transform([text]).toarray()
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text_tensor = torch.tensor(text_vectorized, dtype=torch.float32)
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output = model(text_tensor)
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prediction = 1 if output.item() > 0.5 else 0
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return "Positive" if prediction == 1 else "Negative"
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print(predict_sentiment("I really enjoyed this movie!"))
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print(predict_sentiment("This was the worst experience ever."))
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print(predict_sentiment("It was just okay, nothing special."))
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print(predict_sentiment("Absolutely loved the storyline!"))
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