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|Title:||OPAL at SemEval Task 4: the Challenge of Porting a Sentiment Analysis System to the "Real" World|
|Publisher:||Association for Computational Linguistics|
|Type:||Articles in periodicals and books|
|Abstract:||Sentiment analysis has become a well-established task in Natural Language Pro-cessing. As such, a high variety of methods have been proposed to tackle it, for different types of texts, text levels, languages, domains and formality levels. Although state-of-the-art systems have obtained promising results, a big challenge that still remains is to port the systems to the “real world” – i.e. to implement systems that are running around the clock, dealing with information of heterogeneous na-ture, from different domains, written in differ-ent styles and diverse in formality levels. The present paper describes our efforts to imple-ment such a system, using a variety of strate-gies to homogenize the input and comparing various approaches to tackle the task. Specifi-cally, we are tackling the task using two dif-ferent approaches: a) one that is unsu-pervised, based on dictionaries of sentiment-bearing words and heuristics to compute final polarity of the text considered; b) the second, supervised, trained on previously annotated data from different domains. For both ap-proaches, the data is first normalized and the slang is replaced with its expanded version.|
|JRC Directorate:||Space, Security and Migration|
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