Abstract: Customer feedback analysis plays a crucial role in helping organizations improve their products and services. Using Natural Language Processing (NLP) techniques, this project converts unstructured customer feedback into meaningful insights. The analysis utilizes tools such as NLTK, along with models like Bag of Words (BoW) and advanced deep learning frameworks such as Transformers. The process starts with data preprocessing steps like tokenization, removal of stop words, and lemmatization, efficiently handled by the NLTK library. The Bag of Words model transforms text into numerical data for sentiment classification and topic identification, though it lacks the ability to grasp context. To overcome this, Transformers are employed, offering contextual understanding and accurate sentiment detection. By combining traditional methods like BoW with the sophisticated capabilities of Transformers, this project ensures precise and scalable analysis of customer feedback. This integration enables companies to address user concerns promptly and enhance customer satisfaction.
Keywords: Automated Review System, Semantic Analysis, Natural Language Processing (NLP), NLTK (Natural Language Toolkit), Bag of Words (BoW), Transformers, Tokenization
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DOI:
10.17148/IJARCCE.2025.14234
