how sentiment analysis by python or R
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Hi @student_compute,
In addition to the solution of @kayman, I propose a Python script using the "textblob" library.
From your text attribute, this script delivers a polarity between -1 and +1 where :
-1 (negative) < polarity < +1 (positive).
To execute this script, you have to set the name of your text attribute (with quotes) in the Set Macros operator :
The process :
<?xml version="1.0" encoding="UTF-8"?><process version="9.0.000-BETA4">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="9.0.000-BETA4" expanded="true" name="Process">
<process expanded="true">
<operator activated="true" class="social_media:search_twitter" compatibility="9.0.000-BETA" expanded="true" height="68" name="Search Twitter" width="90" x="112" y="85">
<parameter key="connection" value="dkk"/>
<parameter key="query" value="iphone"/>
<parameter key="limit" value="20"/>
<parameter key="language" value="en"/>
</operator>
<operator activated="true" class="set_macros" compatibility="9.0.000-BETA4" expanded="true" height="82" name="Set Macros" width="90" x="246" y="85">
<list key="macros">
<parameter key="textAttribute" value="'Text'"/>
</list>
</operator>
<operator activated="true" class="python_scripting:execute_python" compatibility="7.4.000" expanded="true" height="82" name="Execute Python" width="90" x="380" y="85">
<parameter key="script" value="import pandas from textblob import TextBlob textAtt = %{textAttribute} # rm_main is a mandatory function, # the number of arguments has to be the number of input ports (can be none) def sent(text) : testimonial = TextBlob(str(text)) sentiment = testimonial.sentiment.polarity return sentiment def rm_main(data): data['polarity'] =data[textAtt].apply(sent) return data "/>
</operator>
<connect from_op="Search Twitter" from_port="output" to_op="Set Macros" to_port="through 1"/>
<connect from_op="Set Macros" from_port="through 1" to_op="Execute Python" to_port="input 1"/>
<connect from_op="Execute Python" from_port="output 1" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
</process>
Regards,
Lionel
Hello
Thank you so much
I used
But I do not know how to show the polarity of sentences based on scores
look
Could this be the case?
Thanks
lionelderkrikor dear
Thank you
------------------
Hellokeyman
I've used your code. But he did not know the package NLTK
How do I download this package and introduce RapidMiner
I use Anacanda. I installed the textblob package but I can not package it
May I help how to do to install?
Thank you
Hi,
Here the screenshots relativ to LDA :
Regards,
Lionel
Hello Sorry, I raised the topic again I tried a lot. Do nltk I installed it. But there is an error in the run. Which I myself could not solve. Can anyone help me? And about The amounts of AVGs reported on the LDA output can be explained to me. What is their use? Thanks for all your help
I like to use the Vader sentiment part of the NLTK toolkit. It works pretty well with social data (sentiment analysis will always remain a bit of a challenge) and gives a bit more than the usual possitive / negative indications
Attached sample uses this framework, the example chops the response by sentence and gives the 'vibe' per sentence. I typically use this method to ensure also mixed data get's covered well. But of course you could also use it on the full data.
What I provided was like this ;
What it returns is as follows :
The more negative or possitive the compound value (range -1 to +1), the more likely it will be that the sentiment of a given sentence is equally negative or possitive