The Altair Community is migrating to a new platform to provide a better experience for you. In preparation for the migration, the Altair Community is on read-only mode from October 28 - November 6, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here

k-nn clasifier

ArnoGArnoG Member Posts: 22 Contributor II
in Help
I am using a K-nn clasifier to perform a sentiment analysis on review texts. I export the results to an excel sheet. For every month (cumlative) I create a bar graph showing the results, Basically the bar graph shows the # of positive and negative reviews.

But till my suprise the results of the sentiment analysis difference per month. For the sentiment analysis I am using exacrly the same model, and exactly the same data & training set. When I run the analyses for january a certain review is labeled positive, when I run the same analyses till february, the same review in january is now labeled as negative.

Is there a way to prefend this?

Best regards,

Arno

Answers

  • RalfKlinkenbergRalfKlinkenberg Employee-RapidMiner, RapidMiner Certified Analyst, RapidMiner Certified Expert, RMResearcher, Member, Unconfirmed, University Professor Posts: 68 RM Founder
    Hi Arno,

    Are you sure that you use exactly the same training data set for both applications of k-NN in January and February?

    If you share your RapidMiner process, we could better check for the reason for this process behaviour.

    Best regards,
    Ralf
  • ArnoGArnoG Member Posts: 22 Contributor II
    Hi Ralf,
    Thanks for your response!

    I am completely sure that I didn't change the training set.

    As an example I used following process:

    <?xml version="1.0" encoding="UTF-8" standalone="no"?>
    <process version="6.0.003">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="6.0.003" expanded="true" name="Process">
        <process expanded="true">
          <operator activated="true" class="read_excel" compatibility="6.0.003" expanded="true" height="60" name="Results" width="90" x="45" y="210">
            <parameter key="excel_file" value="C:\Improve Your Business\Qing\Rapidminer\testing knn.xls"/>
            <parameter key="sheet_number" value="2"/>
            <parameter key="imported_cell_range" value="A1:A18"/>
            <parameter key="first_row_as_names" value="false"/>
            <list key="annotations">
              <parameter key="0" value="Name"/>
            </list>
            <list key="data_set_meta_data_information">
              <parameter key="0" value="Test reviews.true.text.attribute"/>
            </list>
          </operator>
          <operator activated="true" class="select_attributes" compatibility="6.0.003" expanded="true" height="76" name="Select Attributes" width="90" x="313" y="210">
            <parameter key="attribute_filter_type" value="subset"/>
            <parameter key="attributes" value="text|sent|Review|Prijs|Test reviews"/>
          </operator>
          <operator activated="true" class="set_role" compatibility="6.0.003" expanded="true" height="76" name="Set Role" width="90" x="514" y="210">
            <parameter key="attribute_name" value="Test reviews"/>
            <list key="set_additional_roles"/>
          </operator>
          <operator activated="true" class="text:process_document_from_data" compatibility="5.3.002" expanded="true" height="76" name="Process Documents from Data (2)" width="90" x="648" y="210">
            <parameter key="keep_text" value="true"/>
            <parameter key="prune_method" value="absolute"/>
            <parameter key="prune_below_absolute" value="2"/>
            <parameter key="prune_above_absolute" value="999"/>
            <list key="specify_weights"/>
            <process expanded="true">
              <operator activated="true" class="text:tokenize" compatibility="5.3.002" expanded="true" height="60" name="Tokenize (2)" width="90" x="112" y="30"/>
              <operator activated="true" class="text:transform_cases" compatibility="5.3.002" expanded="true" height="60" name="Transform Cases (4)" width="90" x="246" y="30"/>
              <operator activated="true" class="text:filter_stopwords_dictionary" compatibility="5.3.002" expanded="true" height="76" name="Filter Stopwords (4)" width="90" x="380" y="30">
                <parameter key="file" value="C:\Improve Your Business\Qing\Rapidminer\nederlandse stopwoordenlijst.txt"/>
              </operator>
              <operator activated="false" class="text:stem_snowball" compatibility="5.3.002" expanded="true" height="60" name="Stem (4)" width="90" x="514" y="120">
                <parameter key="language" value="Dutch"/>
              </operator>
              <operator activated="true" class="text:filter_by_length" compatibility="5.3.002" expanded="true" height="60" name="Filter Tokens (3)" width="90" x="648" y="30">
                <parameter key="min_chars" value="2"/>
              </operator>
              <operator activated="false" class="text:generate_n_grams_terms" compatibility="5.3.002" expanded="true" height="60" name="Generate n-Grams (2)" width="90" x="782" y="120"/>
              <connect from_port="document" to_op="Tokenize (2)" to_port="document"/>
              <connect from_op="Tokenize (2)" from_port="document" to_op="Transform Cases (4)" to_port="document"/>
              <connect from_op="Transform Cases (4)" from_port="document" to_op="Filter Stopwords (4)" to_port="document"/>
              <connect from_op="Filter Stopwords (4)" from_port="document" to_op="Filter Tokens (3)" to_port="document"/>
              <connect from_op="Filter Tokens (3)" 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="read_excel" compatibility="6.0.003" expanded="true" height="60" name="Training set" width="90" x="45" y="30">
            <parameter key="excel_file" value="C:\Improve Your Business\Qing\Rapidminer\testing knn.xls"/>
            <parameter key="imported_cell_range" value="A1:B243"/>
            <parameter key="first_row_as_names" value="false"/>
            <list key="annotations">
              <parameter key="0" value="Name"/>
            </list>
            <list key="data_set_meta_data_information">
              <parameter key="0" value="Prijs.true.text.attribute"/>
              <parameter key="1" value="B.true.polynominal.label"/>
            </list>
          </operator>
          <operator activated="true" class="select_attributes" compatibility="6.0.003" expanded="true" height="76" name="Select Attributes (2)" width="90" x="313" y="30">
            <parameter key="attribute_filter_type" value="subset"/>
            <parameter key="attributes" value="sent|text|training set|sen|Prijs"/>
          </operator>
          <operator activated="true" class="set_role" compatibility="6.0.003" expanded="true" height="76" name="Set Role (2)" width="90" x="514" y="30">
            <parameter key="attribute_name" value="Prijs"/>
            <list key="set_additional_roles"/>
          </operator>
          <operator activated="true" class="text:process_document_from_data" compatibility="5.3.002" expanded="true" height="76" name="Process Documents from Data" width="90" x="648" y="30">
            <parameter key="keep_text" value="true"/>
            <parameter key="prune_method" value="absolute"/>
            <parameter key="prune_below_absolute" value="2"/>
            <parameter key="prune_above_absolute" value="999"/>
            <list key="specify_weights"/>
            <process expanded="true">
              <operator activated="true" class="text:tokenize" compatibility="5.3.002" expanded="true" height="60" name="Tokenize" width="90" x="45" y="30">
                <parameter key="characters" value=".:?!"/>
              </operator>
              <operator activated="true" class="text:transform_cases" compatibility="5.3.002" expanded="true" height="60" name="Transform Cases" width="90" x="179" y="30"/>
              <operator activated="true" class="text:filter_stopwords_dictionary" compatibility="5.3.002" expanded="true" height="76" name="Filter Stopwords (3)" width="90" x="313" y="30">
                <parameter key="file" value="C:\Improve Your Business\Qing\Rapidminer\nederlandse stopwoordenlijst.txt"/>
              </operator>
              <operator activated="true" class="text:filter_by_length" compatibility="5.3.002" expanded="true" height="60" name="Filter Tokens (by Length)" width="90" x="581" y="30">
                <parameter key="min_chars" value="2"/>
              </operator>
              <connect from_port="document" to_op="Tokenize" to_port="document"/>
              <connect from_op="Tokenize" from_port="document" to_op="Transform Cases" to_port="document"/>
              <connect from_op="Transform Cases" from_port="document" to_op="Filter Stopwords (3)" to_port="document"/>
              <connect from_op="Filter Stopwords (3)" from_port="document" to_op="Filter Tokens (by Length)" to_port="document"/>
              <connect from_op="Filter Tokens (by Length)" 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="k_nn" compatibility="6.0.003" expanded="true" height="76" name="k-NN (2)" width="90" x="782" y="30">
            <parameter key="k" value="3"/>
            <parameter key="weighted_vote" value="true"/>
            <parameter key="measure_types" value="NumericalMeasures"/>
            <parameter key="numerical_measure" value="CosineSimilarity"/>
          </operator>
          <operator activated="true" class="apply_model" compatibility="6.0.003" expanded="true" height="76" name="Apply Model (2)" width="90" x="916" y="120">
            <list key="application_parameters"/>
          </operator>
          <connect from_op="Results" from_port="output" to_op="Select Attributes" to_port="example set input"/>
          <connect from_op="Select 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="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="Training set" 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="Set Role (2)" to_port="example set input"/>
          <connect from_op="Set Role (2)" 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="k-NN (2)" to_port="training set"/>
          <connect from_op="k-NN (2)" from_port="model" to_op="Apply Model (2)" to_port="model"/>
          <connect from_op="Apply Model (2)" from_port="labelled data" 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>


    I run the process for data till april and one till may.

    Images below shows the results from running the two datasets

    Till April
    image


    Till May:
    image

    As you can see is the review "goede prijs" in the run till april predicted neg and in the run till may pos.

    I used exactly the same RM process and exactly the same training data.

    How can I get different results?

    Best Regards,

    Arno
Sign In or Register to comment.