Sentiment Analysis of Reviews

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2021-01-01
Authors
Chitla, Pravalika Ravikumar
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Anthony Townsend
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Information Systems and Business Analytics
In today’s business landscape, information systems and business analytics are pivotal elements that drive success. Information systems form the digital foundation of modern enterprises, while business analytics involves the strategic analysis of data to extract meaningful insights. Information systems have the power to create and restructure industries, empower individuals and firms, and dramatically reduce costs. Business analytics empowers organizations to make precise, data-driven decisions that optimize operations, enhance strategies, and fuel overall growth. Explore these essential fields to understand how data and technology come together, providing the knowledge needed to make informed decisions and achieve remarkable outcomes.
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The process of understanding a given text's emotion that one can use to analyze the product/service reviews is called sentiment analysis. Furthermore, classify them as either Positive, Neutral, or Negative sentiments. Learning the human masses' sentiments towards distinct entities and products facilitates better contextual advertisements, recommendation systems, and market trends analysis. Sentiment Analytics extract sense from human speech and have the power to suggest business insights into large amounts of data such as the opinions of customers, insights that inspire business decisions. Sentiment analysis systems allow companies to make sense of the sea of unstructured text by automating business processes, getting actionable insights, and saving hours of manual data processing by making businesses more efficient. I have chosen the Yelp dataset focusing on reviews of restaurants in Illinois to determine whether they are positive or negative sentiments and develop a model for their classification. This research project concentrates on predicting whether customers have a positive or negative attitude towards a restaurant. I have built a Machine learning model LSTM and used other classifier models such as Naïve Bayes, Bernoulli's, Logistic Regression, Stochastic Gradient Descent, and Support Vector Machine to train the models. I have used evaluation matrices such as prediction, accuracy, recall, and F_1 score to evaluate the classifier's performance on the test sample. Based on the evaluation matrix results, the goal is to find trends in how positive and negative reviews are written and come up with a model for their classification. The results show that SVM is the best model for classifying the reviews to either positive or negative as it gives the highest precision compared to the other classification models.

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Fri Jan 01 00:00:00 UTC 2021