Improving the sentiment classification of stock tweets

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منو

" Improving the sentiment classification of stock tweets "
Li, Sheng

Document Type	:	Latin Dissertation
Record Number	:	895662
Doc. No	:	TLets809906
Main Entry	:	University of Birmingham
Title & Author	:	Improving the sentiment classification of stock tweets\ Li, Sheng
College	:	University of Birmingham
Date	:	2014
Degree	:	Thesis (Ph.D.)
student score	:	2014
Abstract	:	This research focuses on improving stock tweet sentiment classification accuracy with the addition of the linguistic features of stock tweets. Stock prediction based on social media data has been popular in recent years, but none of the previous studies have provided a comprehensive understanding of the linguistic features of stock tweets. Hence, applying a simple statistical model to classifying the sentiment of stock tweets has reached a bottleneck. Thus, after analysing the linguistic features of stock tweets, this research used these features to train four machine learning classifiers. Each of them showed an improvement, and the best one achieved a 9.7% improvement compared to the baseline model. The main contributions of this research are fivefold: (a) it provides an in-depth linguistic analysis of stock tweets; (b) it gives a clear and comprehensive definition of stock tweets; (c) it provides a simple but effective way to automatically identify stock tweets; (d) it provides a simple but effective method of generating a localised sentiment keyword list; and (e) it demonstrates a significant improvement of stock tweet sentiment classification accuracy.
Subject	:	HG Finance; HM Sociology; P Philology. Linguistics
Added Entry	:	University of Birmingham