Sentiment Analysis in Financial News
With the growth of social media, millions of financial news flow every day through the Web. This makes monitoring and interpreting what is happening in the financial world an extremely difficult task. Moreover, as Liu and Zhang (2012) pointed out, it is also known that human analysis of text information is subject to considerable biases. Therefore, a system for automatic detection of sentiment is extremely useful. This was the main motivation behind this work. We have decided to develop a system that can analyse news in the financial domain. As textual data can be very noisy, text pre processing techniques were applied to the news articles (e.g. stopwords removal, stemming) Afterwards, the news were classified as positive, negative or neutral, and a series of studies were carried out to improve the classification results. The method exploited several publicly available lexicons Opinion Lexicon, OpinionFinder, SentiWordNet, AFINN and NRC. Moreover, we have merged some of these lexicons to see if the results could be improved. Besides, a negation handling technique developed by Pang et al. (2002) was also applied. Additionally, some words mainly from the financial world were added to the available lexicons. We have thus obtained enriched lexicons. All experiments were evaluated using usual performance evaluation measures (e.g. Micro F1 However, as sentiment classification can be seen as a problem of classifying ordinal data, an evaluation using cost sensitive analysis was carried out. That is, different costs were applied to different types of error. We have obtained several interesting results. We have identified combinations of