Developer Upgrades Waste Complaint Classifier to Handle Semantic Gaps in Language

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A developer building an AI-based waste complaint classifier found that their TF-IDF and Logistic Regression model struggled to link semantically similar phrases like 'full bin' and 'overflowing container' because the two use different words. The system was trained on 1,000 labeled complaints across five categories, including Missed Pickup, Full Bin, and Illegal Dumping, using an 80/20 train-test split. To address the semantic limitation, the developer began exploring word embedding techniques, starting with Word2Vec, which represents words as vectors and can group contextually related terms like 'waste,' 'garbage,' and 'rubbish' closer together. GloVe and BERT embeddings are also being evaluated, with BERT offering the added advantage of contextual representations where a word's meaning can shift based on its surrounding sentence. The developer plans to compare all three approaches using consistent evaluation metrics to determine which best serves the classifier.

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