Topological Data Analysis (TDA) refers to a collection of methods that find the structure of shapes in data. Although recently, TDA methods have been used in many areas of data mining, it has not been widely applied to text mining tasks. In most text processing algorithms, the order in which different entities appear or co-appear is being lost. Assuming these lost orders are informative features of the data, TDA may play a significant role in the resulted gap on text processing state of the art. Once provided, the topology of different entities through a textual document may reveal some additive information regarding the document that is not reflected in any other features from conventional text processing methods. In this paper, we introduce a novel approach that hires TDA in text processing in order to capture and use the topology of different same-type entities in textual documents. First, we will show how to extract some topological signatures in the text using persistent homology-i.e., a TDA tool that captures topological signature of data cloud. Then we will show how to utilize these signatures for text classification.

Sentiment analysis is the process of identifying the opinion expressed in text. Recently, it has been used to study behavioral finance, and in particular the effect of opinions and emotions on economic or financial decisions. In this paper, we use a public dataset of labeled tweets that has been labeled by Amazon Mechanical Turk and then we propose a baseline classification model. Then, by using Granger causality of both sentiment datasets with the different stocks, we shows that there is causality between social media and stock market returns (in both directions) for many stocks. Finally, We evaluate this causality analysis by showing that in the event of a specific news on certain dates, there are evidences of trending the same news on Twitter for that stock.

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Sentiment analysis is the process of identifying the opinion expressed in text. Recently it has been used to study behavioral finance, and in particular the effect of opinions and emotions on economic or financial decisions. SemEval-2017 task 5 focuses on the financial market as the domain for sentiment analysis of text; specifically, task 5, subtask 1 focuses on financial tweets about stock symbols. In this paper, we describe a machine learning classifier for binary classification of financial tweets. We used natural language processing techniques and the random forest algorithm to train our model, and tuned it for the training dataset of Task 5, subtask 1. Our system achieves the 7th rank on the leaderboard of the task.

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