diff --git a/sentiment-analysis/sentiment-analysis.py b/sentiment-analysis/sentiment-analysis.py
new file mode 100644
index 0000000..3fa0094
--- /dev/null
+++ b/sentiment-analysis/sentiment-analysis.py
@@ -0,0 +1,48 @@
+import os
+import pandas as pd
+import torch
+from transformers import DistilBertTokenizerFast
+from torch.utils.data import DataLoader
+from transformers import DistilBertForSequenceClassification
+from tqdm import tqdm
+
+# Importing the csv dataset.
+case = '...'
+df_data = pd.read_csv(f'../datasets/{case}.csv')
+print(len(df_data))
+
+# Preparing the dataset for the network this includes tokenization, encoding and creating dataloaders.
+model_name = 'distilbert-base-uncased'
+tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
+
+encodings = tokenizer(df_data['text'].tolist(), truncation=True, padding=True, return_tensors='pt')
+
+dataset = torch.utils.data.TensorDataset(
+    encodings['input_ids'], 
+    encodings['attention_mask']
+)
+dataloader = DataLoader(dataset, batch_size=45, shuffle=False)
+
+# Loading the model.
+model= torch.load('models/bert-aclimdb.pth')
+
+# Using the model to perform sentiment analysis on the dataset.
+model.eval()
+list_predicted_scores = []
+
+for batch in tqdm(dataloader):
+    with torch.no_grad():
+        input_ids, attention_mask = batch
+
+        # Obtaining the sentiment score.
+        output = model(input_ids=input_ids, attention_mask=attention_mask)
+        predicted_scores = output.logits.view(-1)
+
+        # Writing the sentiment score to the list.
+        list_predicted_scores.extend(predicted_scores.tolist())
+
+# Inserting the sentiment score in the dataset.
+df_data['s'] = list_predicted_scores
+
+# Exporting the dataframe to a csv dataset.
+df_data.to_csv(f'../datasets/{case}-s.csv', index=False)
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