Classification of articles fetched from the web as describing stock (trending) increase or decrease
You can use a deep learning network to generate sentiment analysis. Pretrained models can be found which have been trained using vectorized word lists which are commonly associated with positivity and negativity. However, these may not generalize well to your specific use case. 

Due to the historic nature of stocks you can easily compile your own database from online sources. For example, you can construct instances in your dataset as follows. Tokenize all the words found in articles pertaining to Google for February 5th 2001, then the target label can be the stock value for February 7th 2001. You can choose the time lag as you please. If you do this for a large amount of instances, your dataset should be able to contain some notion of what words found in articles will cause a stock price to rise or lower.