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All of my prediction row are the same...

User: "Mustafa_AVDAN"
Mustafa_AVDAN
New Altair Community Member
Updated by Jocelyn

Hi,Im new on rapid miner;

at like tittle , all of my predictions are the same,but ı dont know?

all my predictions are negative when ı used naive bayes .

all my predictions are neutral when ı used decision tree.

ı have attached some screen capture about my train set or my result table.Please someone help me...

I just wanna do sentiment analysis on twitter data but ı coulnt do it...And my train set include 92 examples(ı know that isnt enough for the train set) But my train set was just 2 or 3 negative sentences but like I said;

when ı used naive bayes,all predictions were negative,but WHY?

PLEASE HELP ME...

Regards

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    User: "Thomas_Ott"
    Thomas_Ott
    New Altair Community Member
    Accepted Answer

    Here is a very simple process that you can build off. This is how I would start.

     

    <?xml version="1.0" encoding="UTF-8"?><process version="7.6.003">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="7.6.003" expanded="true" name="Process">
        <process expanded="true">
          <operator activated="true" class="social_media:search_twitter" compatibility="7.3.000" expanded="true" height="68" name="Search Twitter" width="90" x="45" y="34">
            <parameter key="connection" value="Twitter - Studio Connection"/>
            <parameter key="query" value="#tesla"/>
            <parameter key="locale" value="en"/>
          </operator>
          <operator activated="true" class="generate_attributes" compatibility="7.6.003" expanded="true" height="82" name="Generate Attributes" width="90" x="179" y="34">
            <list key="function_descriptions">
              <parameter key="Sentiment" value="if([Retweet-Count]&gt;20,&quot;Positive&quot;,&quot;Negative&quot;)"/>
            </list>
            <description align="center" color="transparent" colored="false" width="126">Create Fake Sentiment (add your sentiment labels)</description>
          </operator>
          <operator activated="true" class="set_role" compatibility="7.6.003" expanded="true" height="82" name="Set Role" width="90" x="313" y="34">
            <parameter key="attribute_name" value="Sentiment"/>
            <parameter key="target_role" value="label"/>
            <list key="set_additional_roles"/>
          </operator>
          <operator activated="true" class="select_attributes" compatibility="7.6.003" expanded="true" height="82" name="Select Attributes" width="90" x="447" y="34">
            <parameter key="attribute_filter_type" value="subset"/>
            <parameter key="attributes" value="Text|Sentiment"/>
          </operator>
          <operator activated="true" class="nominal_to_text" compatibility="7.6.003" expanded="true" height="82" name="Nominal to Text" width="90" x="581" y="34">
            <parameter key="attribute_filter_type" value="single"/>
            <parameter key="attribute" value="Text"/>
          </operator>
          <operator activated="true" class="text:process_document_from_data" compatibility="7.5.000" expanded="true" height="82" name="Process Documents from Data" width="90" x="715" y="34">
            <list key="specify_weights"/>
            <process expanded="true">
              <operator activated="true" class="text:tokenize" compatibility="7.5.000" expanded="true" height="68" name="Tokenize" width="90" x="112" y="34"/>
              <connect from_port="document" to_op="Tokenize" to_port="document"/>
              <connect from_op="Tokenize" from_port="document" to_port="document 1"/>
              <portSpacing port="source_document" spacing="0"/>
              <portSpacing port="sink_document 1" spacing="0"/>
              <portSpacing port="sink_document 2" spacing="0"/>
            </process>
          </operator>
          <operator activated="true" class="concurrency:cross_validation" compatibility="7.6.003" expanded="true" height="145" name="Validation" width="90" x="849" y="34">
            <parameter key="sampling_type" value="shuffled sampling"/>
            <process expanded="true">
              <operator activated="true" class="naive_bayes" compatibility="7.6.003" expanded="true" height="82" name="Naive Bayes" width="90" x="250" y="34"/>
              <connect from_port="training set" to_op="Naive Bayes" to_port="training set"/>
              <connect from_op="Naive Bayes" from_port="model" to_port="model"/>
              <portSpacing port="source_training set" spacing="0"/>
              <portSpacing port="sink_model" spacing="0"/>
              <portSpacing port="sink_through 1" spacing="0"/>
              <description align="left" color="green" colored="true" height="113" resized="true" width="284" x="104" y="200">Builds a model on the current training data set (90 % of the data by default, 10 times).&lt;br&gt;&lt;br&gt;Make sure that you only put numerical attributes into a linear regression!</description>
            </process>
            <process expanded="true">
              <operator activated="true" class="apply_model" compatibility="7.6.003" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="performance" compatibility="7.6.003" expanded="true" height="82" name="Performance" width="90" x="179" y="34"/>
              <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="performance 1"/>
              <connect from_op="Performance" from_port="example set" to_port="test set results"/>
              <portSpacing port="source_model" spacing="0"/>
              <portSpacing port="source_test set" spacing="0"/>
              <portSpacing port="source_through 1" spacing="0"/>
              <portSpacing port="sink_test set results" spacing="0"/>
              <portSpacing port="sink_performance 1" spacing="0"/>
              <portSpacing port="sink_performance 2" spacing="0"/>
              <description align="left" color="blue" colored="true" height="107" resized="true" width="333" x="28" y="139">Applies the model built from the training data set on the current test set (10 % by default).&lt;br/&gt;The Performance operator calculates performance indicators and sends them to the operator result.</description>
            </process>
            <description align="center" color="transparent" colored="false" width="126">A cross validation including a linear regression.</description>
          </operator>
          <operator activated="true" class="social_media:search_twitter" compatibility="7.3.000" expanded="true" height="68" name="Search Twitter (2)" width="90" x="45" y="289">
            <parameter key="connection" value="Twitter - Studio Connection"/>
            <parameter key="query" value="#tesla"/>
            <parameter key="locale" value="en"/>
          </operator>
          <operator activated="true" class="select_attributes" compatibility="7.6.003" expanded="true" height="82" name="Select Attributes (2)" width="90" x="246" y="289">
            <parameter key="attribute_filter_type" value="subset"/>
            <parameter key="attributes" value="Text|Sentiment"/>
          </operator>
          <operator activated="true" class="nominal_to_text" compatibility="7.6.003" expanded="true" height="82" name="Nominal to Text (2)" width="90" x="380" y="289">
            <parameter key="attribute_filter_type" value="single"/>
            <parameter key="attribute" value="Text"/>
          </operator>
          <operator activated="true" class="text:process_document_from_data" compatibility="7.5.000" expanded="true" height="82" name="Process Documents from Data (2)" width="90" x="849" y="289">
            <list key="specify_weights"/>
            <process expanded="true">
              <operator activated="true" class="text:tokenize" compatibility="7.5.000" expanded="true" height="68" name="Tokenize (2)" width="90" x="112" y="34"/>
              <connect from_port="document" to_op="Tokenize (2)" to_port="document"/>
              <connect from_op="Tokenize (2)" from_port="document" to_port="document 1"/>
              <portSpacing port="source_document" spacing="0"/>
              <portSpacing port="sink_document 1" spacing="0"/>
              <portSpacing port="sink_document 2" spacing="0"/>
            </process>
          </operator>
          <operator activated="true" class="apply_model" compatibility="7.6.003" expanded="true" height="82" name="Apply Model (2)" width="90" x="1050" y="289">
            <list key="application_parameters"/>
          </operator>
          <connect from_op="Search Twitter" from_port="output" to_op="Generate Attributes" to_port="example set input"/>
          <connect from_op="Generate Attributes" from_port="example set output" to_op="Set Role" to_port="example set input"/>
          <connect from_op="Set Role" from_port="example set output" to_op="Select Attributes" to_port="example set input"/>
          <connect from_op="Select Attributes" from_port="example set output" to_op="Nominal to Text" to_port="example set input"/>
          <connect from_op="Nominal to Text" from_port="example set output" to_op="Process Documents from Data" to_port="example set"/>
          <connect from_op="Process Documents from Data" from_port="example set" to_op="Validation" to_port="example set"/>
          <connect from_op="Process Documents from Data" from_port="word list" to_op="Process Documents from Data (2)" to_port="word list"/>
          <connect from_op="Validation" from_port="model" to_op="Apply Model (2)" to_port="model"/>
          <connect from_op="Validation" from_port="performance 1" to_port="result 1"/>
          <connect from_op="Search Twitter (2)" from_port="output" to_op="Select Attributes (2)" to_port="example set input"/>
          <connect from_op="Select Attributes (2)" from_port="example set output" to_op="Nominal to Text (2)" to_port="example set input"/>
          <connect from_op="Nominal to Text (2)" from_port="example set output" to_op="Process Documents from Data (2)" to_port="example set"/>
          <connect from_op="Process Documents from Data (2)" from_port="example set" to_op="Apply Model (2)" to_port="unlabelled data"/>
          <connect from_op="Apply Model (2)" from_port="labelled data" to_port="result 2"/>
          <portSpacing port="source_input 1" spacing="0"/>
          <portSpacing port="sink_result 1" spacing="0"/>
          <portSpacing port="sink_result 2" spacing="0"/>
          <portSpacing port="sink_result 3" spacing="0"/>
        </process>
      </operator>
    </process>
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