Tweet Sentiment, Sentiment Trend, and a Comparison with Financial Trend Indicators.
MetadataVis full innførsel
Background: As Twitter has become a global microblogging site, it s influ-ence in the stock market has become noticeable. This makes tweets an interest-ing medium for gathering sentiment. A sentiment that might influence trendsin 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 themodern trading algorithms.Another interesting aspect is the role of Twitter in sentiment analysis. Andhow Twitters role as a data source influences trends in the stock market.Data and Experiments: Twitter is used as the data source. It provides easyaccess, lots of data, and many possibilities to use available metadata. To findthe sentiment of a tweet we use two methods, counting positive and negativewords(bag of words), and classifiers (SVM and Naive Bayes). We use movingaverage(MA) and average directional index(ADX) as trend indicators. We cal-culate MA and ADX with data from Oslo stock exchange, and we created ourown indicators, based on MA and ADX, using data from Twitter. Then wecompare the graphs.Findings: We explore the usage of lists of words, dictionaries, in sentimentanalysis. And we look at data retrieval from Twitter and the trend we cancreate 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 andthe sentiment indicators.