Introduction
In today’s digital age, where opinions and experiences are shared at the click of a button, understanding customer sentiment has become more crucial than ever. As businesses strive to provide exceptional products and services, they turn to the wealth of customer feedback available online to gain insights into customer satisfaction, preferences, and areas for improvement. This project delves into the realm of sentiment analysis, a powerful technique that unlocks the emotional and contextual nuances embedded within customer reviews. More specifically, it focuses on restaurant reviews classification using a sentiment analysis tool as well as three powerful machine learning algorithms.
Importance of Classifying Customer Reviews
Customer reviews have evolved into a significant barometer for gauging customer satisfaction and informing business decisions. By analyzing customer feedback, businesses can identify what delights customers and what might require adjustments. Sentiment analysis, a subset of Natural Language Processing, equips us with the tools to automatically determine whether a review reflects positive, negative, or neutral sentiment. This process goes beyond simple keyword counting; it dissects the intricate linguistic patterns to unveil the emotions, opinions, and experiences hidden within the words.
Among the diverse realms of customer reviews, restaurant reviews stand out as a rich source of insights. Dining experiences are inherently personal and often emotionally charged. Customers not only share their thoughts on the food’s taste and quality but also on the ambiance, service, and overall experience. Discerning the sentiment expressed in these reviews can provide restaurateurs with invaluable insights into what aspects of their establishment are resonating with customers and what areas might require improvement.
Project Overview
Using a dataset on restaurant reviews downloaded on Kaggle (500 negative + 500 positive reviews), I implemented text mining operations which include removing noise, converting text to lowercase, removing stopwords, and lemmatization in preparation for analysis. I also used wordclouds to visualise the differences between negative and positive reviews. I also used the VADER sentiment analysis tool to calculate sentiment scores for each review and classify it as positive or negative. After this, I used the Random Forest, Naive-Bayes, and SVM classifiers with tuned hyperparameters to classify the sentiment expressed in each review. The best performing algorithm (SVM) was able to correctly classify 78% of the reviews as positive or negative.
The notebook below shows the entire project step by step.