Analyzing massive, noisy and short microblogs is a very challenging task where traditional sentiment analysis and classification methods are not easily applicable due to inherent characteristics such social media content. Sentiment analysis, also known as opinion mining, is a mechanism for understanding the natural disposition that people possess towards a specific topic. Therefore, it is very important to consider the user context that usually indicates that microblogs posted by the same person tend to have the same sentiment label. One of the main research issue is how to predict twitter sentiment as regards a topic on social media? In this paper, we propose a sentiment mining approach based on sentiment analysis and supervised machine learning principles to the tweets extracted from Twitter. The originality of the suggested approach is that classification does not rely on tweet text to detect polarity, but it depends on users' past text content. Experimental validation is conducted on a tweet corpus taken from data of SemEval 2016. These tweets talk about several topics, and are annotated in advance at the level of sentiment polarity. We have collected the past tweets of each author of the collection tweets. As an initial experiment in the prediction of user sentiment on a topic, based on his past, the results obtained seem acceptable, and could be improved in future work.
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