Sentiment Analysis Using Machine Learning
Document Type
Oral Presentation
Campus where you would like to present
Ellensburg
Event Website
https://digitalcommons.cwu.edu/source
Start Date
16-5-2019
End Date
16-5-2019
Abstract
Sentiment analysis is the process of computationally evaluating spoken or written language to determine if the message that is being conveyed yields a positive, negative or neutral opinion. It is really important because nowadays, companies receive a massive amount of reviews about their products, brands, websites, customer service etc... and the way this information is handled can be critical for their success. They therefore, must find a way to process this information quickly and accurately so that they can respond to customers’ needs on time. Having this in mind, we developed a software using machine learning tools, that is able to predict if a text will have a positive or negative impact on the reader. We used the TF-IDF (term’s frequency-Inverse Document frequency) technique to pre-process the input text (convert it to numbers) and the MLP (multilayer-perceptron) to classify the reviews. We obtained a sample of 25000 movie reviews from the IMDB website, trained our model using 75% of them and tested it with the rest(25%). Following this process, we were able to correctly classify 91% of the reviews that were used for testing. The software is written in Python using the machine learning library ”Scikit-Learn.” It is ready to be used and is able to classify a large amount of reviews in a short period of time.
Winner, Outstanding Oral Presentation, School of Graduate Studies and Research.
Recommended Citation
Nkouanga, Hermann Yepdjio; Dewan, Shivika; and Lin, Chao Huang, "Sentiment Analysis Using Machine Learning" (2019). Symposium Of University Research and Creative Expression (SOURCE). 174.
https://digitalcommons.cwu.edu/source/2019/Oralpres/174
Department/Program
Computer Science
Slides for SOURCE 2019 presentation Nkouanga
Sentiment Analysis Using Machine Learning
Ellensburg
Sentiment analysis is the process of computationally evaluating spoken or written language to determine if the message that is being conveyed yields a positive, negative or neutral opinion. It is really important because nowadays, companies receive a massive amount of reviews about their products, brands, websites, customer service etc... and the way this information is handled can be critical for their success. They therefore, must find a way to process this information quickly and accurately so that they can respond to customers’ needs on time. Having this in mind, we developed a software using machine learning tools, that is able to predict if a text will have a positive or negative impact on the reader. We used the TF-IDF (term’s frequency-Inverse Document frequency) technique to pre-process the input text (convert it to numbers) and the MLP (multilayer-perceptron) to classify the reviews. We obtained a sample of 25000 movie reviews from the IMDB website, trained our model using 75% of them and tested it with the rest(25%). Following this process, we were able to correctly classify 91% of the reviews that were used for testing. The software is written in Python using the machine learning library ”Scikit-Learn.” It is ready to be used and is able to classify a large amount of reviews in a short period of time.
Winner, Outstanding Oral Presentation, School of Graduate Studies and Research.
https://digitalcommons.cwu.edu/source/2019/Oralpres/174
Faculty Mentor(s)
Razvan Andonie