In rapidminer studio polynomial variable4/10/2023 ![]() ![]() ![]() These variables get passed into the stored procedure which allows it to be applied to both tables. To be more flexible, I have also created two variables inside the package which hold the table names the package populates. The whole SSIS solution is as per below (Control and Data Flow) The records from the reference data went into ‘Twitter_Training_Data’ table whereas the feeds we will mine were inserted into ‘Twitter_Test_Data’ table. I imported and percentage sampled (for smaller dataset) the data from the two pre-processed files using a small SSIS package into my local MS SQL Server database together with the file containing Twitter feeds created using Zapier (see previous post) which hasn’t been analysed yet. These are quite large in size and probably overkill for this project but, in theory, the more data we provide for model to learn to distinguish between NEGATIVE and POSITIVE categories, the better it should perform. Next, I downloaded two files containing already sentiment-tagged Twitter feeds. To get started, I created two tables which will house the data, one for reference data which already contains sentiment information and one for the data extracted from Twitter which hasn’t been analysed yet using the following code:ĬREATE TABLE. Most of the files I am using in this project can be downloaded from HERE (including all the SQL code, YouTube video, sample data files etc.). To do this, I will employ free software called RapidMiner which can be downloaded from HERE as well as two separate data sets of already pre-classified tweets for model learning and Microsoft SQL Server for some data scrubbing and storage engine. whether a specific Twitter feed can be considered as negative or positive. In this series-final post, I would like to show you how this data can be analyzed for sentiment i.e. In part one, I explained how anyone can extract Twitter data into Google Docs spread sheet and then transfer is into a local environment using two different methods. In this post I would like to build on what was developed in first iteration to this two part series describing Twitter data extraction and sentiment analysis. Extracting and Mining Twitter Data Using Zapier, RapidMiner and Google/Microsoft Tools ![]()
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