BBC OA 面试真题解析：Classify Customer Feedback（客户反馈情感分类）

Classify Customer Feedback

Given a dataset of customer feedback classified as Positive or Negative, build a deep neural network to classify feedback accurately. A review is categorized based on tone, words, length, and the style of writing by the customers.

Datasets

• train.csv — data used to train the model
• test.csv — data used to test predictions
• submissions.csv — populate this file with the results
• sample_submission.csv — sample reference of submission data file

Task

Submit the predictions on the test dataset using your optimized model.

For each record in the test set (test.csv), predict the value of the feedback variable.

You should submit a CSV file with a header row and one row per test entry.

The file submissions.csv should have exactly 2 columns:

• customer_review — customer review
• feedback — feedback (0-Negative, 1-Positive)

Build a neural network to classify customer feedback.

Experiment with different preprocessing methods, numbers of layers, types of layers, activation functions, and any other relevant parameters. Compile the model by specifying the loss function and optimizer. Ensure that the model is not overfitting.

Assess model performance on train.csv using the F1 Score metric. The model will be tested for robustness using a different dataset.

这道 BBC OA 题目本质上是一个二分类文本情感分析任务：根据客户评论内容预测反馈是 0（Negative）还是 1（Positive）。解题时通常先对文本做基础清洗与分词，再把文本转成序列并进行 padding，然后构建一个适合文本分类的神经网络，例如 Embedding + BiLSTM/GRU 或简单 CNN，再用二元交叉熵作为损失函数、Adam 作为优化器进行训练。题目特别强调要用 F1 Score 评估，因此不能只看准确率，通常需要在验证集上调节阈值、控制过拟合、尝试 dropout、早停和不同的网络结构，以获得对测试集更稳健的效果。