Date of Award
5-2018
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer and Information Technology
Committee Chair
Julia Rayz
Committee Member 1
Baijian Yang
Committee Member 2
John Springer
Abstract
Sentiment analysis is useful for multiple tasks including customer satisfaction metrics, identifying market trends for any industry or products, analyzing reviews from social media comments. This thesis highlights the importance of sentiment analysis, provides a summary of seminal works and different approaches towards sentiment analysis. It aims to address sentiment analysis on financial news and microblogs by classifying textual data from financial news and microblogs as positive or negative. Sentiment analysis is performed by making use of paragraph vectors and logistic regression in this thesis and it aims to compare it with previously performed approaches to performing analysis and help researchers in this field. This approach achieves state of the art results for the dataset used in this research. It also presents an insightful analysis of the results of this approach.
Recommended Citation
Talekar, Chinmay, "Sentiment Analysis on Financial News and Microblogs" (2018). Open Access Theses. 1462.
https://docs.lib.purdue.edu/open_access_theses/1462