Sentiment Analysis of Reviews

dc.contributor.author Chitla, Pravalika Ravikumar
dc.contributor.department Information Systems and Business Analytics
dc.contributor.majorProfessor Anthony Townsend
dc.date 2021-06-02T13:16:34.000
dc.date.accessioned 2021-08-14T03:33:31Z
dc.date.available 2021-08-14T03:33:31Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2021
dc.date.embargo 2021-04-22
dc.date.issued 2021-01-01
dc.description.abstract <p><strong>Abstract</strong></p> <p>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.</p>
dc.format.mimetype PDF
dc.identifier archive/lib.dr.iastate.edu/creativecomponents/721/
dc.identifier.articleid 1883
dc.identifier.contextkey 22615705
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath creativecomponents/721
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/qzoDG10w
dc.source.bitstream archive/lib.dr.iastate.edu/creativecomponents/721/Creative_Component_Project___Sentiment_Analysis_of_Reviews.pdf|||Sat Jan 15 01:44:40 UTC 2022
dc.subject.disciplines Business Analytics
dc.subject.keywords Analysis
dc.subject.keywords Data Analysis
dc.subject.keywords Business Analysis
dc.subject.keywords Python
dc.subject.keywords Machine Learning Models
dc.title Sentiment Analysis of Reviews
dc.type article
dc.type.genre creativecomponent
dspace.entity.type Publication
relation.isOrgUnitOfPublication 0099bcd5-3121-4f25-813d-0ec68d96243f
thesis.degree.discipline Information Systems
thesis.degree.level creativecomponent
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