Semantic Analysis
Uncovering Hidden Patterns in Unstructured Data - Amazon App Review Analysis
Project
Challenge
Businesses generate massive amounts of textual data—customer reviews, social media feedback, emails, and reports. However, without the right techniques, this unstructured data remains untapped, making it difficult to extract meaningful insights that drive strategic decisions.
Goal
This project leverages Natural Language Processing (NLP) and text mining techniques to analyse unstructured business data. The focus is on transforming raw text into actionable insights using tokenisation, sentiment analysis, topic modelling, and word embeddings—all implemented through Python and Jupyter Notebooks. Process Data Preprocessing – Cleaning and preparing text data through tokenisation, stop-word removal, and stemming. Exploratory Analysis – Using word clouds, frequency distribution, and co-occurrence analysis. Sentiment Analysis – Applying machine learning and lexicon-based techniques to classify sentiment. Topic Modelling – Implementing Latent Dirichlet Allocation (LDA) to identify key themes in datasets. Text Classification – Training models to categorise documents based on industry-specific labels.
Result
Negative reviews from customers has been increasing in numbers bearing signals for certain quality issues to be attended to not to lose overall customer satisfaction. Detected main issues needful to be checked and resolved are; - Refund process for returned items and/or cancellations, - Customer service contact availability and communication with customers, - App bugs and errors such as “Something went wrong” error, - Delivery services; untimely and wrong address deliveries, - Technical issues related to Alexa and galaxy devices. Additionally, we can conclude trigram level frequency distribution can be very helpful to gain insights from such text data consisting of reviews. The project successfully showcases how text mining can unlock valuable business insights, helping organisations make data-driven decisions. By applying these techniques, companies can improve customer feedback analysis, market trend identification, and automated document classification.