Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Sentiment Analysis of Equity Analyst Research Reports using Convolutional Neural Networks
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
2019 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Natural language processing, a subfield of artificial intelligence and computer science, has recently been of great research interest due to the vast amount of information created on the internet in the modern era. One of the main natural language processing areas concerns sentiment analysis. This is a field that studies the polarity of human natural language and generally tries to categorize it as either positive, negative or neutral. In this thesis, sentiment analysis has been applied to research reports written by equity analysts. The objective has been to investigate if there exist a distinct distribution of the reports and if one is able to classify sentiment in these reports. The thesis consist of two parts; firstly investigating possibilities on how to divide the reports into different sentiment labelling regimes and secondly categorizing the sentiment using machine learning techniques. Logistic regression as well as several convolutional neural network structures has been used to classify the sentiment. Working with textual data requires the mapping of text to real valued values called features. Several feature extraction methods has been investigated including Bag of Words, term frequency-inverse document frequency and Word2vec. Out of the tested labelling regimes, classifying the documents using upgrades and downgrades of report recommendation shows the most promising potential. For this regime, the convolutional neural network architectures outperform logistic regression by a significant margin. Out of the networks tested, a double input channel utilizing two different Word2vec representations performs the best. The two different representations originate from different sources; one from the set of equity research reports and the other trained by the Google Brain team on an extensive Google news data set. This suggests that using one representation that represent topic specific words and one that is better at representing more common words enhances classification performance.

Place, publisher, year, edition, pages
2019. , p. 43
Series
UPTEC F, ISSN 1401-5757 ; 19044
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:uu:diva-388586OAI: oai:DiVA.org:uu-388586DiVA, id: diva2:1334054
External cooperation
SEB
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2019-07-02 Created: 2019-07-02 Last updated: 2019-07-02Bibliographically approved

Open Access in DiVA

Sentiment Analysis of Equity Analyst Research Reports using Convolutional Neural Networks(932 kB)47 downloads
File information
File name FULLTEXT01.pdfFile size 932 kBChecksum SHA-512
f05e57cfdd6a554908db85a24f7fd45317b2b8d8e31d6411383d998654b00a793efa9bb4fd546e1a39158fd1d697f84b827504693b5c9fce083e477df8bff12c
Type fulltextMimetype application/pdf

By organisation
Division of Scientific Computing
Language Technology (Computational Linguistics)

Search outside of DiVA

GoogleGoogle Scholar
Total: 47 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 114 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf