Stocks-related messages on social media have several interesting properties regarding the sentiment analysis (SA) task. On the one hand, the analysis is particularly challenging, because of frequent typos, bad grammar, and idiosyncratic expressions specific to the domain and media. On the other hand, stocksrelated messages primarily refer to the state of specific entities-companies and their stocks, at specific times (of sending). This state is an objective property and even has a measurable numeric characteristic, namely the stock price. Given a large dataset of twitter messages, we can create two separate "views" on the dataset by analyzing messages' text and external properties separately. With this, we can expand the coverage of generic SA tools and learn new sentiment expressions. In this paper, we experiment with this learning method, comparing several types of general SA tools and sets of external properties. The method is shown to produce significant improvement in accuracy.
Sentiment relevance detection problems occur when there is a sentiment expression in a text, and there is the question of whether or not the expression is related to a given entity or, more generally, to a given situation. The paper discusses variants of the problem, and shows that it is distinct from other somewhat similar problems occurring in the field of sentiment analysis and opinion mining. We experimentally demonstrate that using the information about relevancy significantly affects the final sentiment evaluation of the entities. We then compare a set of different algorithms for solving the relevance detection problem. The most accurate results are achieved by algorithms that use certain document-level information about the target entities. We show that this information can be accurately extracted using supervised classification methods.
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