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This paper suggests a universal method for creating keyword lists (bag-of-words) for classifying texts concerned with a certain context (e.g. movies, technical products, stocks) as positive or negative (sentiment analysis). The method consists of two steps. The first step is identifying the context with the help of a taxonomy, the second step const...
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Citations
... Following the state-of-the-art research as established in the previous section, in the next step we applied sentiment analysis to microblog messages, forum posts, and traditional news using a Naïve Bayes classifier with an adapted bag-of-words in combination with part-of-speech tagging to find negations and spam filtering based on keywords. The basis of the applied sentiment methodology can be found in Krauss and Nann and Schoder (2012). One key finding in this study indicates that the quality of sentiment recognition depends on how specific the sentiment analysis algorithms are adjusted to the analyzed context. ...
This work examines the predictive power of public data by aggregating information from multiple online sources. Our sources include microblogging sites like Twitter, online message boards like Yahoo! Finance, and traditional news articles. The subject of prediction are daily stock price movements from Standard & Poor's 500 index (S&P 500) during a period from June 2011 to November 2011. To forecast price movements we filter messages by stocks, apply state-of-the-art sentiment analysis to message texts, and aggregate message sentiments to generate trading signals for daily buy and sell decisions. We evaluate prediction quality through a simple trading model considering real-world limitations like transaction costs or broker commission fees. Considering 833 virtual trades, our model outperformed the S&P 500 and achieved a positive return on investment of up to ~0.49% per trade or ~0.24% when adjusted by market, depending on supposed trading costs.
This work examines the predictive power of public data by aggregating information from multiple online sources. Our sources include microblogging sites like Twitter, online message boards like Yahoo! Finance, and traditional news articles. The subject of prediction are daily stock price movements from Standard & Poor's 500 index (S&P 500) during a period from June 2011 to November 2011. To forecast price movements we filter messages by stocks, apply state-of-the-art sentiment analysis to message texts, and aggregate message sentiments to generate trading signals for daily buy and sell decisions. We evaluate prediction quality through a simple trading model considering real-world limitations like transaction costs or broker commission fees. Considering 833 virtual trades, our model outperformed the S&P 500 and achieved a positive return on investment of up to ~0.49% per trade or ~0.24% when adjusted by market, depending on supposed trading costs.
