Abstract
Nowadays, sentiment analysis is becoming a very important issue of research. This paper present experimentation on sentiment analysis based on subjective lexicon method. This experimentation is tested over French tweets using "Public Opinion Knowledge (POK)" platform. POK is a platform consists in getting public opinion orientation from text extracted from social network and blogs, which we have developed and presented in previous papers. There are three algorithms as classifiers, which are based on Natural Language Processing Tools. The first is based on OpenNLP, the second on CoreNLP and the third on dependency analysis implemented by CoreNLP. Each classifier consists of three steps, which are Part of Speech Tagging (POS), word polarity classification and sentiment classification algorithm. On the one hand, the results are used to evaluate the use of OpenNLP and CoreNLP, on other, they draw to make a comparison between lexicon and machine-learning approaches. So, experimentation leads us to conclude that tools of sentiment analysis based on lexicon are much performant than those based on machine learning and they can reach a rate of precision of 70% and F-measure of 0.7. Also, we conclude that CoreNLP is more efficient than OpenNLP by 3% of precision, this fact is due to the efficiency of Part of Speech tagging algorithms.
Highlights
IntroductionPeople post a real-time message about their sentiments on subjects that concern them
Social Networks have evolved and become the first source of information
We present an experimentation of this model using two Natural Language Processing tools, which are openNLP and coreNLP
Summary
People post a real-time message about their sentiments on subjects that concern them. All those messages constitute a huge amount of information that can be used in many fields and many ways, for example to get feedback of product and service, to measure the popularity of a brand or even as aid of politician to make adequate decisions. This kind of information represents the public opinion. Our main goal is to propose a new way of getting public opinion based on social-network content and a Big Data approach (Rhouati et al, 2016b)
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