The development of semantic sentiment analyser utilising sentiment composition for financial news
Sentiment analysis is a technique to determine and extract subjective information from source materials. This thesis studies the effectiveness of a lexicon-based sentiment analysis that used sentiment composition rules and semantic similarity techniques to perform polarity classification for fina...
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my-ums-ep.119092021-01-21T06:52:14Z The development of semantic sentiment analyser utilising sentiment composition for financial news 2016 Tan, Li Im Sentiment analysis is a technique to determine and extract subjective information from source materials. This thesis studies the effectiveness of a lexicon-based sentiment analysis that used sentiment composition rules and semantic similarity techniques to perform polarity classification for financial news articles. This method utilized a prior polarity lexicon to determine the polarity of the analysed text. The semantic sentiment analyser is developed to assist investors in their stock investment by providing them the news sentiment as a source of references in their investment decision. This work compares and combines a few existing sentiment analysis methods to determine the positive and negative classification of the news articles. There is set of 893 financial news articles were collected for experiment purposes from early of year 2013 until June 2013. The research project started off with the development of the Baseline Sentiment Analyser based on existing sentiment composition rules and a mathematical formula namely Positivity/Negativity ratio to determine the sentiment value of the analysed text. This sentiment value is used to determine the polarity of the financial news article. In this model, a phrase extraction tool is needed for phrase extraction according to the Part-of-Speech of the text. Various data mining methods such as stemming and lemmatization algorithms were used to produce different representations of data. These sets of data are combined with the different phrase extraction tools to work out the best combination for the lexicon matching task. Next, an Enhanced Sentiment Analyser with a new set of sentiment composition rules is proposed. This set of sentiment composition rules made used of the verb-phrase sentiment composition, the verb-noun phrase sentiment composition, the noun-verb phrase sentiment composition, the conjunction ""but"" sentiment composition, and the negation rule which include more polarity shifters. Finally, this sentiment analyser is further improved and into a Semantic Sentiment Analyser. Three metrics (HSO, LESK, and LIN) were used to find the semantic similarity between input word and matched words as well as to perform polarity tagging and their performances were compared. WordNet was used as the lexical resources in determining the relationship between two words in this task. The best metric found in this task which is HSO was applied to the proposed Semantic Sentiment Analyser to calculate the semantic similarity between words and to perform polarity tagging to the matched pair that yielded the highest semantic similarity value. This task optimized the word with polarity every time a new financial news article is analysed. While analyzing the financial news article, the prior polarity lexicon is expanded as well. The performance of the proposed Semantic Sentiment Analyser was evaluated and showed promising results in classifying positive and negative news. 2016 Thesis https://eprints.ums.edu.my/id/eprint/11909/ https://eprints.ums.edu.my/id/eprint/11909/1/The%20development%20of%20semantic.pdf text en public https://eprints.ums.edu.my/id/eprint/11909/7/The%20development%20of%20semantic%20sentiment%20analyser%20utilising%20sentiment%20composition%20for%20financial%20news.pdf text en validuser other masters Universiti Malaysia Sabah Faculty of Computing and Informatics |
| institution |
Universiti Malaysia Sabah |
| collection |
UMS Institutional Repository |
| language |
English English |
| description |
Sentiment analysis is a technique to determine and extract subjective information
from source materials. This thesis studies the effectiveness of a lexicon-based
sentiment analysis that used sentiment composition rules and semantic similarity
techniques to perform polarity classification for financial news articles. This method
utilized a prior polarity lexicon to determine the polarity of the analysed text. The
semantic sentiment analyser is developed to assist investors in their stock
investment by providing them the news sentiment as a source of references in their
investment decision. This work compares and combines a few existing sentiment
analysis methods to determine the positive and negative classification of the news
articles. There is set of 893 financial news articles were collected for experiment
purposes from early of year 2013 until June 2013. The research project started off
with the development of the Baseline Sentiment Analyser based on existing
sentiment composition rules and a mathematical formula namely Positivity/Negativity
ratio to determine the sentiment value of the analysed text. This sentiment value is
used to determine the polarity of the financial news article. In this model, a phrase
extraction tool is needed for phrase extraction according to the Part-of-Speech of the
text. Various data mining methods such as stemming and lemmatization algorithms
were used to produce different representations of data. These sets of data are
combined with the different phrase extraction tools to work out the best combination
for the lexicon matching task. Next, an Enhanced Sentiment Analyser with a new set
of sentiment composition rules is proposed. This set of sentiment composition rules
made used of the verb-phrase sentiment composition, the verb-noun phrase
sentiment composition, the noun-verb phrase sentiment composition, the
conjunction ""but"" sentiment composition, and the negation rule which include more
polarity shifters. Finally, this sentiment analyser is further improved and into a
Semantic Sentiment Analyser. Three metrics (HSO, LESK, and LIN) were used to find
the semantic similarity between input word and matched words as well as to
perform polarity tagging and their performances were compared. WordNet was used
as the lexical resources in determining the relationship between two words in this
task. The best metric found in this task which is HSO was applied to the proposed
Semantic Sentiment Analyser to calculate the semantic similarity between words and
to perform polarity tagging to the matched pair that yielded the highest semantic
similarity value. This task optimized the word with polarity every time a new financial
news article is analysed. While analyzing the financial news article, the prior polarity
lexicon is expanded as well. The performance of the proposed Semantic Sentiment
Analyser was evaluated and showed promising results in classifying positive and
negative news. |
| format |
Thesis |
| qualification_name |
other |
| qualification_level |
Master's degree |
| author |
Tan, Li Im |
| spellingShingle |
Tan, Li Im The development of semantic sentiment analyser utilising sentiment composition for financial news |
| author_facet |
Tan, Li Im |
| author_sort |
Tan, Li Im |
| title |
The development of semantic
sentiment analyser utilising
sentiment composition for financial
news |
| title_short |
The development of semantic
sentiment analyser utilising
sentiment composition for financial
news |
| title_full |
The development of semantic
sentiment analyser utilising
sentiment composition for financial
news |
| title_fullStr |
The development of semantic
sentiment analyser utilising
sentiment composition for financial
news |
| title_full_unstemmed |
The development of semantic
sentiment analyser utilising
sentiment composition for financial
news |
| title_sort |
development of semantic
sentiment analyser utilising
sentiment composition for financial
news |
| granting_institution |
Universiti Malaysia Sabah |
| granting_department |
Faculty of Computing and Informatics |
| publishDate |
2016 |
| url |
https://eprints.ums.edu.my/id/eprint/11909/1/The%20development%20of%20semantic.pdf https://eprints.ums.edu.my/id/eprint/11909/7/The%20development%20of%20semantic%20sentiment%20analyser%20utilising%20sentiment%20composition%20for%20financial%20news.pdf |
| _version_ |
1747836439095148544 |