🎉Community Raffle - Win $25

An exclusive raffle opportunity for active members like you! Complete your profile, answer questions and get your first accepted badge to enter the raffle.
Join and Win

All of my prediction row are the same...

Mustafa_AVDANUser: "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

Find more posts tagged with

AI Studio
Getting Started
Sort by:
1 - 1 of 11
    Thomas_OttUser: "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>
    View in context
Quick Links