عنوان

Improving the sentiment classification of stock tweets

شماره

TLets809906

عنوان اصلي

Improving the sentiment classification of stock tweets

نام عام مواد

[Thesis]

نام نخستين پديدآور

Li, Sheng

نام ناشر، پخش کننده و غيره

University of Birmingham

تاریخ نشرو بخش و غیره

2014

جزئيات پايان نامه و نوع درجه آن

Thesis (Ph.D.)

امتياز متن

2014

متن يادداشت

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.

موضوع مستند نشده

HG Finance; HM Sociology; P Philology. Linguistics

مستند نام اشخاص تاييد نشده

University of Birmingham

مستند نام تنالگان تاييد نشده

University of Birmingham

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y