A Framework and practical implementation for sentiment.

Recursive Deep Learning. The models in this family are variations and extensions of unsupervised and supervised recursive neural networks (RNNs) which generalize deep and feature learning ideas to hierarchical structures. The RNN models of this thesis obtain state of the art performance on paraphrase detection, sentiment analysis, rela-.

A Vector Space Approach for Aspect-Based Sentiment Analysis by Abdulaziz Alghunaim B.S., Massachusetts Institute of Technology (2015) Submitted to the Department of Electrical Engineering and Computer Science in Partial Ful llment of the Requirements for the Degree of Master of Engineering in Electrical Engineering and Computer Science at the.

ANALYSIS OF NEWS SENTIMENT AND ITS APPLICATION TO FINANCE By Xiang Yu A thesis submitted for the degree of Doctor of Philosophy School of Information Systems, Computing and Mathematics, Brunel University 6 May 2014. Dedication: To the loving memory of my mother. i Abstract We report our investigation of how news stories influence the behaviour of tradable financial assets, in particular.

Microblogs and social media platforms are now considered among the most popular forms of online communication. Through a platform like Twitter, much information reflecting people's opinions and attitudes is published and shared among users on a daily basis. This has recently brought great opportunities to companies interested in tracking and monitoring the reputation of their brands and.

An Introduction to Aspect Based Sentiment Analysis 1.1 Subject and contribution of this thesis Aspect Based Sentiment Analysis (ABSA) systems receive as input a set of texts (e.g., product reviews or messages from social media) discussing a particular entity (e.g., a new model of a mobile phone). The systems attempt to detect the main (e.g.

Sentiment Analysis on Multi-view Social Data by Teng Niu Thesis submitted to the Faculty of Graduate and Postdoctoral Studies In partial ful llment of the requirements For the M.C.S. degree in Computer Science School of Electrical Engineering and Computer Science Faculty of Engineering University of Ottawa c Teng Niu, Ottawa, Canada, 2016.

Sentiment analysis on students’ real-time feedback Nabeela Altrabsheh The thesis is submitted in partial fulfilment of the requirements for the award of the degree of Doctor of Philosophy of the University of Portsmouth February 2016. Abstract Previous literature identifies that students’ real-time feedback is important in the learning process. There are numerous studies that have.

Sentiment Analysis and Resources for Informal Arabic Text on Social Media Maher Itani A thesis submitted in partial fulfilment of the requirements of Sheffield Hallam University for the degree of Doctor of Philosophy August 2018. 2 Abstract Online content posted by Arab users on social networks does not generally abide by the grammatical and spelling rules. These posts, or comments, are.

In this thesis, we study topic-oriented sentiment analysis on Blogs and Microblogs. Specif-ically, we address challenges brought by the presence of topic drift and informal terms and expressions. Topic drift is a common phenomenon on the blogosphere. We propose to combat this problem by reducing the noise for the sentiment classi ers. In our.

Sentiment Analysis or Opinion Mining is the computational treatment of opinions, sentiments and subjectivity of text. In this report, we take a look at the various challenges and applications of.

The need for the automatic analysis of opinions in written texts, which has been growing in recent years in several domains, has made Sentiment Analysis a very popular field (Liu 2012). In this area, systems have been traditionally classifying sentences as positive or negative only in accordance to the sentiment that words most frequently assume (e.g. “angry” negative, “beautiful.

The main contribution of this thesis is the development and evaluation of a graph-based sentiment analysis system that a) can cope with the challenges of limited data availability by using semi-supervised and crossdomain approaches b) is able to perform multiclass classi cation and c) achieves highly accurate results which are superior to those.