POLIMENOCAMASTRA-THESIS-2019.pdf (1.63 MB)
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Opinion Mining for Decision Making

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thesis
posted on 06.08.2019, 00:00 by Paolo Polimeno Camastra
This work is concerned with Text Clustering, Sentiment Classification and Transfer Learning in Sentiment Classification. We choose to work in the domain of online reviews because they are suitable for our purposes: the dataset comprises reviews’ texts and labels as well as various categories of products. Labels are needed in order to perform the Supervised Learning part of our work, i.e. Sentiment Classification, while the presence of different product categories enable us to have different domains, such as Electronics, Movies, Clothing, etc., which is a requirement for Transfer Learning. Our main contribution in Text Clustering is in terms of comparison between clusterings obtained after different text vectorizations: tf-idf+Singular Value Decomposition (SVD), LDA and word2vec.

History

Advisor

Di Eugenio, Barbara

Chair

Di Eugenio, Barbara

Department

Computer Science

Degree Grantor

University of Illinois at Chicago

Degree Level

Masters

Committee Member

Parde, Natalie Lanzi, Pier Luca

Submitted date

May 2019

Issue date

06/02/2019

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