Twitter Sentiment Analysis – Part 2. Extracting and Mining Twitter Data Using Zapier, RapidMiner and Google/Microsoft Tools
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. 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 series-final post, I would like to show you how this data can be analyzed for sentiment i.e. whether a specific Twitter feed can be considered as negative or positive. 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. 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 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:
CREATE TABLE [dbo].[Twitter_Training_Data] ([ID] [int] IDENTITY(1,1) NOT NULL, [Feed] [varchar](max) NULL, [Sentiment] [varchar](50) NULL) ON [PRIMARY] GO CREATE TABLE [dbo].[Twitter_Test_Data] ([ID] [int] IDENTITY(1,1) NOT NULL, [Feed] [varchar](max) NULL) ON [PRIMARY] GO
Next, I downloaded two files containing already sentiment-tagged Twitter feeds. 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. 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. 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. The whole SSIS solution is as per below (Control and Data Flow)
To be more flexible, I have also created two variables inside the package which hold the table names the package populates.
These variables get passed into the stored procedure which allows it to be applied to both tables. To pass the variables to the data cleansing stored procedure I assigned the variable name in ‘Parameters Mapping’ property of each of the two Execute SQL Tasks with its corresponding data type to a parameter as per below.
The reason for storing this information in a database rather than a file is simply because once in a table, the data can undergo further cleaning and formatting using the logic implemented in the SQL code. As I found out the hard way, a lot of the strings people put into their tweets contain tons of useless ACSII characters and shortened URL references which only pollute the test data and confuse the model. Also, reading data from a database can incur substantial performance increase. To further clean the data, I created two functions, one to weed out non-alphanumeric characters and one to remove references to URLs. As the classification mining model relies on words which are greater than 2 characters in length and web application generated URLs are mostly comprised of artificially created strings, these characters don not contribute to the overall outcome and in many cases can in fact skew the output. These two functions get used by the stored procedure which executes as the final step of the ETL package. The SQL code for those is as per below.
--CREATE STRING SPLITTING FUNCTION CREATE FUNCTION [dbo].[Split] (@sep VARCHAR(32), @s VARCHAR(MAX)) RETURNS @t TABLE (val VARCHAR(MAX)) AS BEGIN DECLARE @xml XML SET @XML = N'' + REPLACE(@s, @sep, '') + '' INSERT INTO @t(val) SELECT r.value('.','VARCHAR(1000)') as Item FROM @xml.nodes('//root/r') AS RECORDS(r) RETURN END
--CREATE FUNCTION TO REMOVE UNWANTED CHARACTERS CREATE FUNCTION [dbo].[RemoveSpecialChars] (@Input VARCHAR(MAX)) RETURNS VARCHAR(MAX) BEGIN DECLARE @Output VARCHAR(MAX) IF (ISNULL(@Input,'')='') SET @Output = @Input ELSE BEGIN DECLARE @Len INT DECLARE @Counter INT DECLARE @CharCode INT SET @Output = '' SET @Len = LEN(@Input) SET @Counter = 1 WHILE @Counter <= @Len BEGIN SET @CharCode = ASCII(SUBSTRING(@Input, @Counter, 1)) IF @CharCode=32 OR @CharCode BETWEEN 48 and 57 OR @CharCode BETWEEN 65 AND 90 OR @CharCode BETWEEN 97 AND 122 SET @Output = @Output + CHAR(@CharCode) SET @Counter = @Counter + 1 END END RETURN @Output END
--CREATE STRING CLEANING STORED PROCEDURE CREATE PROCEDURE [dbo].[usp_Massage_Twitter_Data] (@table_name NVARCHAR(100)) AS BEGIN BEGIN TRY SET NOCOUNT ON --CREATE CLUSTERED INDEX SQL DECLARE @SQL_Drop_Index NVARCHAR(1000) SET @SQL_Drop_Index = 'IF EXISTS (SELECT name FROM sys.indexes WHERE name = N''IX_Feed_ID'') DROP INDEX IX_Feed_ID ON ' + @table_name + ' CREATE CLUSTERED INDEX IX_Feed_ID ON ' + @Table_Name + ' (ID)'; --REMOVE UNWANTED CHARACTERS SQL DECLARE @SQL_Remove_Chars NVARCHAR(2000) SET @SQL_Remove_Chars = 'UPDATE ' + @Table_Name + ' SET feed = (select dbo.RemoveSpecialChars(feed)) DECLARE @z int DECLARE db_cursor CURSOR FOR SELECT id FROM ' + @Table_Name + ' OPEN db_cursor FETCH NEXT FROM db_cursor INTO @z WHILE @@FETCH_STATUS = 0 BEGIN DECLARE @Combined_String VARCHAR(max); WITH cte(id, val) AS ( SELECT a.id, fs.val FROM ' + @Table_Name + ' a CROSS APPLY dbo.split('' '', feed) AS fs WHERE fs.val NOT LIKE ''%http%'' and a.id = @z) SELECT @Combined_String = COALESCE(@Combined_String + '' '', '''') + val FROM cte UPDATE ' + @Table_Name + ' SET feed = ltrim(rtrim(@Combined_String)) WHERE ' + @Table_Name + '.id = @z SELECT @Combined_String = '''' FETCH NEXT FROM db_cursor INTO @z END CLOSE db_cursor DEALLOCATE db_cursor' --RESEED IDENTITY COLUMN & DELETE EMPTY RECORDS SQL DECLARE @SQL_Reseed_Delete NVARCHAR(1000) SET @SQL_Reseed_Delete = 'IF EXISTS (SELECT c.is_identity FROM sys.tables t JOIN sys.schemas s ON t.schema_id = s.schema_id JOIN sys.Columns c ON c.object_id = t.object_id JOIN sys.Types ty ON ty.system_type_id = c.system_type_id WHERE t.name = ''+ @Table_Name +'' AND s.Name = ''dbo'' AND c.is_identity=1) SET IDENTITY_INSERT ' + @Table_Name + ' ON DELETE FROM ' + @Table_Name + ' WHERE Feed IS NULL or Feed ='''' DBCC CHECKIDENT (' + @Table_Name + ', reseed, 1) SET IDENTITY_INSERT ' + @Table_Name + ' OFF ALTER INDEX IX_Feed_ID ON ' + @Table_Name + ' REORGANIZE' EXECUTE sp_executesql @SQL_Drop_Index EXECUTE sp_executesql @SQL_Remove_Chars EXECUTE sp_executesql @SQL_Reseed_Delete END TRY BEGIN CATCH IF @@TRANCOUNT > 0 BEGIN ROLLBACK TRANSACTION END DECLARE @ErrorMessage NVARCHAR(4000); DECLARE @ErrorSeverity INT; DECLARE @ErrorState INT; SELECT @ErrorMessage = ERROR_MESSAGE(), @ErrorSeverity = ERROR_SEVERITY(), @ErrorState = ERROR_STATE(); RAISERROR ( @ErrorMessage ,-- Message text. @ErrorSeverity ,-- Severity. @ErrorState -- State. ); END CATCH IF @@TRANCOUNT > 0 BEGIN COMMIT TRANSACTION END END
The package extracts Twitter feeds from each of the three CSV files (I downloaded them as XLSX and converted into CSV simply by opening them up in Excel and saving again as CSV as SSIS can be a bit temperamental using Excel as a data source), storing them in assigned tables and performing some data cleansing using the above SQL code.
After both training data and new observations have been loaded into the database it is time to fire up RapidMiner. If you have any experience with ETL applications relying on drag-and-drop transformations functionally you should feel at home using RapidMiner. Using it is similar to building an SSIS routine on a development pane and linking individual transformations to create a logical data flow. To start, we need to create a database connection to read our tweets from SQL Server. There are numerous tutorials on the Internet and a few particularly useful ones on YouTube on how to install JTDE driver on the Windows box to read data from SQL Server instance so I won’t go into details on how to accomplish that. Below is a screen shot of the connection I set up in RapidMiner on my machine.
Finally, it is time to develop the model and the rest of the solution for Twitter data classification. As explaining it step by step with all supporting screen shots would require at least another post, I decided to provide this overview as a short footage hosted on YouTube – see below.
You can also download the original footage with all the complementary files e.g. SQL code, source data files etc. from HERE.
If you enjoyed this post please also check out my earlier version of Twitter sentiment analysis using pure SQL and SSIS which can be viewed from HERE. Finally, if you’re after a more adhoc/playful sentiment analysis tool for Twitter data and (optionally) have some basic knowledge of Python programming language, check out my post on using etcML web based tool under THIS link.http://scuttle.org/bookmarks.php/pass?action=add
This entry was posted on Friday, March 29th, 2013 at 4:54 pm and is filed under SQL, SSIS. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.