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.

BALAHUR-DOBRESCU Alexandra; 

2017-01-16

Association for Computational Linguistics

JRC100957

978-1-941643-95-2,   

https://aclweb.org/anthology/S/S16/,    https://publications.jrc.ec.europa.eu/repository/handle/JRC100957,