and a Comparison with
Financial Trend Indicators.
Background: As Twitter has become a global microblogging site, its influ-
ence in the stock market has become noticeable. This makes tweets an interest-
ing medium for gathering sentiment. A sentiment that might influence trends
in the stock market.
Motivation: If Twitter can be used to predict future prices in the stock mar-
ket the casual investor would gain an advantage over the day-trader and the
modern trading algorithms.
Another interesting aspect is the role of Twitter in sentiment analysis. And
how Twitters role as a data source influences trends in the stock market.
Data and Experiments: Twitter is used as the data source. It provides easy
access, lots of data, and many possibilities to use available metadata. To find
the sentiment of a tweet we use two methods, counting positive and negative
words(bag of words), and classifiers (SVM and Naive Bayes). We use moving
average(MA) and average directional index(ADX) as trend indicators. We cal-
culate MA and ADX with data from Oslo stock exchange, and we created our
own indicators, based on MA and ADX, using data from Twitter. Then we
compare the graphs.
Findings: We explore the usage of lists of words, dictionaries, in sentiment
analysis. And we look at data retrieval from Twitter and the trend we can
create from it. To a varying degree we get positive results with the dictionaries,
while the trend aggregation lacks the finesse and results we hoped for.
Conclusion: Sentiment classification of tweets worked with both bag of words,
and trained classifiers. We also managed to aggregate a trend based on senti-
ment, but we found no correlation between the financial trend indicators and
the sentiment indicators.
Place, publisher, year, edition, pages
Institutt for datateknikk og informasjonsvitenskap , 2014. , 106 p.
IdentifiersURN: urn:nbn:no:ntnu:diva-27032Local ID: ntnudaim:10013OAI: oai:DiVA.org:ntnu-27032DiVA: diva2:756639
Öztürk, Pinar, FørsteamanuensisHolme, Arvid