model for classification
lina
New Altair Community Member
hi to everyone! i'm still working on opinion mining and classification into positive and negative comments.my process is this:
what's wrong about it?i have also tried other available models as well.
thank you in advance!
<?xml version="1.0" encoding="UTF-8" standalone="no"?>could someone tell me which is the appropriate model for classification to choose? i'm using naive bayes as you can see from the code.but the accuracy in the results is 0%!
<process version="5.1.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.1.001" expanded="true" name="Process">
<process expanded="true" height="396" width="687">
<operator activated="true" class="text:process_document_from_file" compatibility="5.1.001" expanded="true" height="76" name="Process Documents from Files" width="90" x="45" y="30">
<list key="text_directories">
<parameter key="positive" value="C:\Users\Fotis-Linaki\Desktop\diplwmatikh\rapidminer\opinion mining\pos"/>
<parameter key="negative" value="C:\Users\Fotis-Linaki\Desktop\diplwmatikh\rapidminer\opinion mining\neg"/>
</list>
<parameter key="encoding" value="UTF-8"/>
<parameter key="vector_creation" value="Term Occurrences"/>
<process expanded="true" height="396" width="705">
<operator activated="true" class="text:transform_cases" compatibility="5.1.001" expanded="true" height="60" name="Transform Cases" width="90" x="45" y="30"/>
<operator activated="true" class="text:tokenize" compatibility="5.1.001" expanded="true" height="60" name="Tokenize" width="90" x="179" y="120"/>
<operator activated="true" class="text:filter_by_length" compatibility="5.1.001" expanded="true" height="60" name="Filter Tokens (by Length)" width="90" x="380" y="120">
<parameter key="min_chars" value="3"/>
</operator>
<operator activated="true" class="text:stem_snowball" compatibility="5.1.001" expanded="true" height="60" name="Stem (Snowball)" width="90" x="514" y="30">
<parameter key="language" value="Spanish"/>
</operator>
<connect from_port="document" to_op="Transform Cases" to_port="document"/>
<connect from_op="Transform Cases" from_port="document" to_op="Tokenize" to_port="document"/>
<connect from_op="Tokenize" from_port="document" to_op="Filter Tokens (by Length)" to_port="document"/>
<connect from_op="Filter Tokens (by Length)" from_port="document" to_op="Stem (Snowball)" to_port="document"/>
<connect from_op="Stem (Snowball)" from_port="document" to_port="document 1"/>
<portSpacing port="source_document" spacing="0"/>
<portSpacing port="sink_document 1" spacing="18"/>
<portSpacing port="sink_document 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="x_validation" compatibility="5.1.001" expanded="true" height="130" name="Validation" width="90" x="246" y="75">
<parameter key="sampling_type" value="linear sampling"/>
<process expanded="true" height="396" width="327">
<operator activated="true" class="naive_bayes_kernel" compatibility="5.1.001" expanded="true" height="76" name="Naive Bayes (Kernel)" width="90" x="119" y="94"/>
<connect from_port="training" to_op="Naive Bayes (Kernel)" to_port="training set"/>
<connect from_op="Naive Bayes (Kernel)" from_port="model" to_port="model"/>
<portSpacing port="source_training" spacing="18"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="36"/>
</process>
<process expanded="true" height="396" width="327">
<operator activated="true" class="apply_model" compatibility="5.1.001" expanded="true" height="76" name="Apply Model" width="90" x="45" y="75">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance" compatibility="5.1.001" expanded="true" height="76" name="Performance" width="90" x="179" y="120"/>
<connect from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="36"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
<portSpacing port="sink_averagable 3" spacing="0"/>
</process>
</operator>
<operator activated="true" class="write_model" compatibility="5.1.001" expanded="true" height="60" name="Write Model" width="90" x="405" y="182">
<parameter key="model_file" value="C:\Users\Fotis-Linaki\Desktop\diplwmatikh\rapidminer\opinion mining\double1.mod"/>
</operator>
<connect from_op="Process Documents from Files" from_port="example set" to_op="Validation" to_port="training"/>
<connect from_op="Validation" from_port="model" to_op="Write Model" to_port="input"/>
<connect from_op="Validation" from_port="training" to_port="result 1"/>
<connect from_op="Validation" from_port="averagable 1" to_port="result 2"/>
<connect from_op="Validation" from_port="averagable 2" to_port="result 3"/>
<portSpacing port="source_input 1" spacing="18"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="72"/>
<portSpacing port="sink_result 3" spacing="0"/>
<portSpacing port="sink_result 4" spacing="0"/>
</process>
</operator>
</process>
what's wrong about it?i have also tried other available models as well.
thank you in advance!
Tagged:
0
Answers
-
sorry for the 2nd post! I fixed sth and now the accuracy is 50% +/-16%. for negative about 66% and positive about 33%.
to be honest, what exactly does it mean? it is about estimation, isn' it?but could somebody explain me in a more detailed way what is it about and what does it mean to my proccess?is it good?i'm afraid not!i'm sorry if i sound silly :-[ !0 -
Hi,
we cannot explain you the basics of data analytics here in this forum. Sorry for that. I would suggest to refer either to a good book about it, or participate in one of the basic training courses we offer.
With kind regards,
Sebastian Land0
