Stock market trending analysis is one of the key research topics in financial analysis. Various theories once highlighted the non-viability of stock market prediction. With the advent of machine learning and Artificial Intelligence (AI), more and more efforts have been devoted to this research area, and predicting the stock market has been demonstrated to be possible. Learning-based methods have been popularly studied for stock price prediction. However, due to the dynamic nature of the stock market and its non-linearity, stock market prediction is still one of the most dificult tasks. With the rise of social networks, huge amount of data is being generated every day and there is a gaining in popularity of incorporating these data into prediction model in the effort to enhance the prediction performance. Therefore, this paper explores the possibilities of the viability of learning-based stock market trending prediction by incorporating social media sentiment analysis. Six machine learning methods including Multi-Layer Perception, Support Vector Machine, Naïve Bayes, Random Forest, Logistic Regression and Extreme Gradient Boosting are selected as the baseline model. The result indicates the possibilities of successful stock market trending prediction and the performance of different learning-based methods is discussed. It is discovered that the distribution of the value of stocks may affect the prediction performance of the methods involved. This research not only demonstrates the merits and weaknesses of different learning-based methods, but also points out that incorporating social opinion is a right direction for improving the performance of stock market trending prediction.