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A new reporting tool developed by Databricks will allow users to provide feedback on their first impressions of a company in as few as 10 words. The new data product, called Twitter Sentiment Analysis, is designed to enable anyone to quickly judge the mood of conversations happening in social media about a particular company. This new tool was built at the request of Databricks co-founder Jay Kreps, who’s been working with Twitter to analyze public moods related to the company. He wants to capture the online buzz and understand when things are going well or going badly for the company. “Sending a company a new product or finding a new supplier is hard and requires a long process of analysis and dozens of yes/no decisions,” says Kreps. “With sentiment analysis, you can now be more nimble and get feedback quickly.” The sentiment analysis algorithm built by Databricks uses a 100-line Python script to analyze all the words in the Twitter stream and classify their sentiment. The tool then generates a report containing a lot of data that captures the sentiment and volume of the content about a company in a particular time period. That report can be shared to the company so they can perform their own analysis and make their own decisions. “We envision that the tool will provide a near real time score based on the public perception, and we believe companies will find it extremely useful for their decision-making process,” says Kreps. “Today, companies often have to wait days or weeks to get an answer to a survey question. Now it’s possible to get data that can help them decide something in a matter of minutes or hours.” It’s still early days for the new tool, but Kreps is hopeful that it will be widely used in the future. While he has seen many uses of sentiment analysis already, the tool being developed at Databricks is the first application that uses a distributed data analytics tool to process Twitter traffic to create a sentiment score. “At Databricks, we have been looking to leverage our DataFrame tool and how the data is distributed,” says Kreps. “Over the last several weeks, we have been experimenting with how to use Twitter data in Databricks to create an open source tool and hope to support this with long-term contracts with Twitter.” Kreps says the sentiment data is a great first step for

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Q: How to combine two matrices whose sizes are determined at runtime in python I am trying to concatenate two matrices whose sizes are determined at runtime. In my application there are two matrices one which holds the indices of a sparse matrix and the other matrix contains the values of the elements of the sparse matrix. All the elements of the two matrices are of the same type. I need to concatenate the two matrices to form a second matrix that will contain the indices of both the sparse matrices. I am using the scipy.sparse.csrmatrix and scipy.sparse.coo_matrix type of matrices. For example here is my code: from scipy.sparse import * from matplotlib import pylab import numpy as np #as in the following example, sample matrices are present, #i have created these matrices at runtime using Matlab a=csr_matrix([[1,2],[3,4]]) b=csr_matrix([[5,6],[7,8]]) c=a+b #obtained this matrix [[1,2] # [5,6]] #obtained this matrix in matrix form. [5,6] print(c.A) #[5 6] #i need a matrix which will contain the indices of both the sparse matrices. In this case it should be print(c.A) [[0 3] [1 2]] #a separate matrix to contain the values of the elements of the concatenated sparse matrix. In this case it should be print(c.B) [[1] [4]] I have researched about row/column indexing but I do not understand how to do it in python. Also how to convert the csr/coo matrix to the above form. I have done it in Matlab: [m, n] = size(a) % permute a’s rows to make it symmetric: a_p = a.’; % permute a’s rows to make it symmetric b_p = b.’; % concatenate the permuted matrices c

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