In this paper we have used sentiment analysis on news articles to see its effect on stock prices. We collected our dataset using Bing API which gave us links to news articles about a specific company. As no pre-existing sentiment dictionary specifically for stock articles exited, we created a specialized sentiment dictionary only meant to analyze stock articles. Two different machine learning algorithms were applied to the dataset and the accuracy of the two was compared. In order to test our results we attached an overall sentiment to each article in our data set which was compared to the predicted sentiment by the algorithm. We also compared the predicted results with the actual change in the stock prices on the market.

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Social media such as Twitter has provided a platform for users to gather and share information and stay updated with the news. However, restriction on the length, informal grammar and vocabulary of the posts pose challenges to perform classification from textual content alone. We propose models based on the Hawkes process (HP) which can naturally incorporate additional cues such as the temporal features and past labels of the posts, along with the textual features for improving short text classification. In particular, we propose a discriminative approach to model text in HP, where the text features parameterize the base intensity and the triggering kernel of the intensity function. This allows textual content to determine influence from past posts and consequently determine the intensity function and class label. Another major contribution is to model the kernel as a neural network function of both time and text, permitting more complex influence functions for Hawkes process. This will maintain the interpretability of Hawkes process models along with the improved function learning capability of the neural networks. The proposed HP models can easily consider pretrained word embeddings to represent text for classification. Experiments on the rumour stance classification problems in social media demonstrate the effectiveness of the proposed HP models.

Social media has provided a platform for users to gather and share information and stay updated with the news. Such networks also provide a platform to users where they can engage in conversations. However, such micro-blogging platforms like Twitter restricts the length of text. Due to paucity of sufficient word occurrences in such posts, classification of this information is a challenging task using standard tools of natural language processing (NLP). Moreover, high complexity and dynamics of the posts in social media makes text classification a challenging problem. However, considering additional cues in the form of past labels and times associated with the post can be potentially helpful for performing text classification in a better way. To address this problem, we propose models based on the Hawkes process (HP) which can naturally incorporate the temporal features and past labels along with textual features for improving short text classification. In particular, we propose a discriminative approach to model text in HP where the text features parameterize the base intensity and/or the triggering kernel. Another major contribution is to consider kernel to be a function of both time and text, and further use a neural network to model the kernel. This enables modeling and effectively learning the text along with the historical influences for tweet classification. We demonstrate the advantages of the proposed techniques on standard benchmarks for rumour stance classification.