Application and Comparison of Neural Networks and Traditional Machine Learning Methods: Analysing Sentiment of Yelp Reviews

Bjarke Dalby Schiøtte, Suheil Fathi Almasri & Helene Diemer Frees

Student thesis: Master thesis

Abstract

Artificial Intelligence systems are getting better and better at understanding natural language. A significant part of understanding natural language lies within the domain of sentiment analysis. The outburst of user-generated content generated on social sites such as Facebook and Yelp can be mined and analysed for sentiment analysis. We perform sentiment analysis on review data from Yelp by comparing the traditional methods Naïve Bayes and Support Vector Machine classifiers, to the more contemporary AI technologies of Recurrent Neural Networks and Convolutional Neural Networks. We conduct our analysis based on our own methodology framework inspired by the CRISP model and the Design Science Research approach. Thus, we develop the machine learning models based on the knowledge we gain from the theoretical grounding and base the findings on our interpretations of the results. We find that the neural network-based models are far superior to the traditional methods on this problem. We believe that this is due to the informal and non-linear nature of the reviews, making it difficult for the traditional methods without a significant amount of feature engineering. The neural networks, however, are non-linear models that are able to find patterns in the data themselves, thus enabling them to classify the sentiment accurately. Thus, in a business situation, where the data consist of non-linear text, the neural networks would be the preferred models as they perform the best compared to the traditional ones.

EducationsMSc in Business Administration and Information Systems, (Graduate Programme) Final Thesis
LanguageEnglish
Publication date2018
Number of pages125
SupervisorsDaniel Hardt