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"Text Classification with SVM"
Contributor II
Hi,
My goal is to optimize sentiment prediction by using SVM insted knn.
Therefor i had to bring up a substep which proves for subjectivity (with a two class Model: subjectivity, nonsub). Each sentence with subjectivity would be relevant for my SVM based approach. Certainly, i'm using SVM to predict the subjectivity. But when i apply the Model on unclassified sentences i receive as a result just on prediction class.
This is my process to create the model. The process to apply the model is down under. Am i missing something?
[tt]<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<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="539" width="1069">
<operator activated="false" class="store" compatibility="5.1.001" expanded="true" height="60" name="Store" width="90" x="916" y="435">
<parameter key="repository_entry" value="01_model_svm"/>
</operator>
<operator activated="false" class="retrieve" compatibility="5.1.001" expanded="true" height="60" name="Retrieve" width="90" x="45" y="30">
<parameter key="repository_entry" value="06_classified_data_binomial"/>
</operator>
<operator activated="false" class="naive_bayes" compatibility="5.1.001" expanded="true" height="76" name="Naive Bayes" width="90" x="514" y="30">
<parameter key="laplace_correction" value="false"/>
</operator>
<operator activated="true" class="retrieve" compatibility="5.1.001" expanded="true" height="60" name="Retrieve (3)" width="90" x="45" y="120">
<parameter key="repository_entry" value="11_classified_data_subjectivity_binomial"/>
</operator>
<operator activated="true" class="text:process_document_from_data" compatibility="5.1.000" expanded="true" height="76" name="Process Documents from Data" width="90" x="246" y="30">
<parameter key="vector_creation" value="Binary Term Occurrences"/>
<parameter key="prune_method" value="absolute"/>
<parameter key="prune_below_absolute" value="2"/>
<parameter key="prune_above_absolute" value="10000"/>
<list key="specify_weights"/>
<process expanded="true" height="537" width="839">
<operator activated="true" class="text:tokenize" compatibility="5.1.000" expanded="true" height="60" name="Tokenize" width="90" x="45" y="30"/>
<operator activated="true" class="text:stem_snowball" compatibility="5.1.000" expanded="true" height="60" name="Stem (Snowball)" width="90" x="447" y="30">
<parameter key="language" value="German"/>
</operator>
<connect from_port="document" to_op="Tokenize" to_port="document"/>
<connect from_op="Tokenize" 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="0"/>
<portSpacing port="sink_document 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="select_attributes" compatibility="5.1.001" expanded="true" height="76" name="Select Attributes" width="90" x="112" y="255">
<parameter key="attribute_filter_type" value="no_missing_values"/>
<parameter key="attributes" value="label|sentence"/>
</operator>
<operator activated="true" class="set_role" compatibility="5.1.001" expanded="true" height="76" name="Set Role" width="90" x="246" y="165">
<parameter key="name" value="label"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="support_vector_machine_libsvm" compatibility="5.1.001" expanded="true" height="76" name="SVM (5)" width="90" x="514" y="345">
<parameter key="kernel_type" value="linear"/>
<parameter key="cache_size" value="40"/>
<parameter key="epsilon" value="0.1"/>
<list key="class_weights"/>
<parameter key="calculate_confidences" value="true"/>
</operator>
<operator activated="true" class="store" compatibility="5.1.001" expanded="true" height="60" name="Store (3)" width="90" x="715" y="300">
<parameter key="repository_entry" value="10_model_no_boosting_svm"/>
</operator>
<operator activated="false" class="k_nn" compatibility="5.1.001" expanded="true" height="76" name="k-NN" width="90" x="648" y="165">
<parameter key="k" value="3"/>
<parameter key="measure_types" value="NumericalMeasures"/>
<parameter key="numerical_measure" value="CosineSimilarity"/>
</operator>
<operator activated="false" class="store" compatibility="5.1.001" expanded="true" height="60" name="Store (2)" width="90" x="782" y="165">
<parameter key="repository_entry" value="//sentiment_prediction/10_model_no_boosting_knn_subjectivity"/>
</operator>
<connect from_op="Retrieve (3)" from_port="output" to_op="Process Documents from Data" to_port="example set"/>
<connect from_op="Process Documents from Data" from_port="example set" 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="SVM (5)" to_port="training set"/>
<connect from_op="SVM (5)" from_port="model" to_op="Store (3)" to_port="input"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
</process>
</operator>
</process>[/tt]
This is the Process to apply the model:
[tt]<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.1.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.1.001" expanded="true" name="Process">
<parameter key="parallelize_main_process" value="true"/>
<process expanded="true" height="469" width="768">
<operator activated="true" class="retrieve" compatibility="5.1.001" expanded="true" height="60" name="JPW_getTestData" width="90" x="45" y="120">
<parameter key="repository_entry" value="//sentiment_prediction/05_test_dataset"/>
</operator>
<operator activated="true" class="retrieve" compatibility="5.1.001" expanded="true" height="60" name="Model SVM#" width="90" x="45" y="255">
<parameter key="repository_entry" value="10_model_no_boosting_svm"/>
</operator>
<operator activated="true" class="text:process_document_from_data" compatibility="5.1.000" expanded="true" height="76" name="Process Documents from Data (2)" width="90" x="447" y="30">
<parameter key="prune_method" value="absolute"/>
<parameter key="prune_below_absolute" value="2"/>
<parameter key="prune_above_absolute" value="10000"/>
<list key="specify_weights"/>
<process expanded="true" height="469" width="763">
<operator activated="true" class="text:tokenize" compatibility="5.1.000" expanded="true" height="60" name="Tokenize (2)" width="90" x="45" y="30"/>
<operator activated="true" class="text:stem_snowball" compatibility="5.1.000" expanded="true" height="60" name="Stem (2)" width="90" x="404" y="30">
<parameter key="language" value="German"/>
</operator>
<connect from_port="document" to_op="Tokenize (2)" to_port="document"/>
<connect from_op="Tokenize (2)" from_port="document" to_op="Stem (2)" to_port="document"/>
<connect from_op="Stem (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="informationExtraction:tree_svm" compatibility="1.0.000" expanded="true" height="94" name="TreeSVM" width="90" x="305" y="396"/>
<operator activated="true" class="apply_model" compatibility="5.1.001" expanded="true" height="76" name="Apply Model" width="90" x="313" y="165">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="multiply" compatibility="5.1.001" expanded="true" height="94" name="Multiply" width="90" x="447" y="345"/>
<operator activated="true" class="select_attributes" compatibility="5.1.001" expanded="true" height="76" name="Select Attributes" width="90" x="514" y="165">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attributes" value="id|prediction(label)|sentence|confidence(neutral)|confidence(positiv)|confidence(negativ)"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<operator activated="true" class="write_database" compatibility="5.1.001" expanded="true" height="60" name="Write Database" width="90" x="648" y="165">
<parameter key="connection" value="192.168.1.124 @sentiment_analysis"/>
<parameter key="table_name" value="sentiment_unclassified_set_subjectivity"/>
<parameter key="overwrite_mode" value="overwrite"/>
</operator>
<connect from_op="JPW_getTestData" from_port="output" to_op="Process Documents from Data (2)" to_port="example set"/>
<connect from_op="Model SVM#" from_port="output" to_op="Apply Model" to_port="model"/>
<connect from_op="Process Documents from Data (2)" from_port="example set" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Multiply" to_port="input"/>
<connect from_op="Multiply" from_port="output 1" to_op="Select Attributes" to_port="example set input"/>
<connect from_op="Multiply" from_port="output 2" to_port="result 1"/>
<connect from_op="Select Attributes" from_port="example set output" to_op="Write Database" to_port="input"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
</process>[/tt]
My goal is to optimize sentiment prediction by using SVM insted knn.
Therefor i had to bring up a substep which proves for subjectivity (with a two class Model: subjectivity, nonsub). Each sentence with subjectivity would be relevant for my SVM based approach. Certainly, i'm using SVM to predict the subjectivity. But when i apply the Model on unclassified sentences i receive as a result just on prediction class.
This is my process to create the model. The process to apply the model is down under. Am i missing something?
[tt]<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<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="539" width="1069">
<operator activated="false" class="store" compatibility="5.1.001" expanded="true" height="60" name="Store" width="90" x="916" y="435">
<parameter key="repository_entry" value="01_model_svm"/>
</operator>
<operator activated="false" class="retrieve" compatibility="5.1.001" expanded="true" height="60" name="Retrieve" width="90" x="45" y="30">
<parameter key="repository_entry" value="06_classified_data_binomial"/>
</operator>
<operator activated="false" class="naive_bayes" compatibility="5.1.001" expanded="true" height="76" name="Naive Bayes" width="90" x="514" y="30">
<parameter key="laplace_correction" value="false"/>
</operator>
<operator activated="true" class="retrieve" compatibility="5.1.001" expanded="true" height="60" name="Retrieve (3)" width="90" x="45" y="120">
<parameter key="repository_entry" value="11_classified_data_subjectivity_binomial"/>
</operator>
<operator activated="true" class="text:process_document_from_data" compatibility="5.1.000" expanded="true" height="76" name="Process Documents from Data" width="90" x="246" y="30">
<parameter key="vector_creation" value="Binary Term Occurrences"/>
<parameter key="prune_method" value="absolute"/>
<parameter key="prune_below_absolute" value="2"/>
<parameter key="prune_above_absolute" value="10000"/>
<list key="specify_weights"/>
<process expanded="true" height="537" width="839">
<operator activated="true" class="text:tokenize" compatibility="5.1.000" expanded="true" height="60" name="Tokenize" width="90" x="45" y="30"/>
<operator activated="true" class="text:stem_snowball" compatibility="5.1.000" expanded="true" height="60" name="Stem (Snowball)" width="90" x="447" y="30">
<parameter key="language" value="German"/>
</operator>
<connect from_port="document" to_op="Tokenize" to_port="document"/>
<connect from_op="Tokenize" 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="0"/>
<portSpacing port="sink_document 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="select_attributes" compatibility="5.1.001" expanded="true" height="76" name="Select Attributes" width="90" x="112" y="255">
<parameter key="attribute_filter_type" value="no_missing_values"/>
<parameter key="attributes" value="label|sentence"/>
</operator>
<operator activated="true" class="set_role" compatibility="5.1.001" expanded="true" height="76" name="Set Role" width="90" x="246" y="165">
<parameter key="name" value="label"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="support_vector_machine_libsvm" compatibility="5.1.001" expanded="true" height="76" name="SVM (5)" width="90" x="514" y="345">
<parameter key="kernel_type" value="linear"/>
<parameter key="cache_size" value="40"/>
<parameter key="epsilon" value="0.1"/>
<list key="class_weights"/>
<parameter key="calculate_confidences" value="true"/>
</operator>
<operator activated="true" class="store" compatibility="5.1.001" expanded="true" height="60" name="Store (3)" width="90" x="715" y="300">
<parameter key="repository_entry" value="10_model_no_boosting_svm"/>
</operator>
<operator activated="false" class="k_nn" compatibility="5.1.001" expanded="true" height="76" name="k-NN" width="90" x="648" y="165">
<parameter key="k" value="3"/>
<parameter key="measure_types" value="NumericalMeasures"/>
<parameter key="numerical_measure" value="CosineSimilarity"/>
</operator>
<operator activated="false" class="store" compatibility="5.1.001" expanded="true" height="60" name="Store (2)" width="90" x="782" y="165">
<parameter key="repository_entry" value="//sentiment_prediction/10_model_no_boosting_knn_subjectivity"/>
</operator>
<connect from_op="Retrieve (3)" from_port="output" to_op="Process Documents from Data" to_port="example set"/>
<connect from_op="Process Documents from Data" from_port="example set" 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="SVM (5)" to_port="training set"/>
<connect from_op="SVM (5)" from_port="model" to_op="Store (3)" to_port="input"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
</process>
</operator>
</process>[/tt]
This is the Process to apply the model:
[tt]<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.1.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.1.001" expanded="true" name="Process">
<parameter key="parallelize_main_process" value="true"/>
<process expanded="true" height="469" width="768">
<operator activated="true" class="retrieve" compatibility="5.1.001" expanded="true" height="60" name="JPW_getTestData" width="90" x="45" y="120">
<parameter key="repository_entry" value="//sentiment_prediction/05_test_dataset"/>
</operator>
<operator activated="true" class="retrieve" compatibility="5.1.001" expanded="true" height="60" name="Model SVM#" width="90" x="45" y="255">
<parameter key="repository_entry" value="10_model_no_boosting_svm"/>
</operator>
<operator activated="true" class="text:process_document_from_data" compatibility="5.1.000" expanded="true" height="76" name="Process Documents from Data (2)" width="90" x="447" y="30">
<parameter key="prune_method" value="absolute"/>
<parameter key="prune_below_absolute" value="2"/>
<parameter key="prune_above_absolute" value="10000"/>
<list key="specify_weights"/>
<process expanded="true" height="469" width="763">
<operator activated="true" class="text:tokenize" compatibility="5.1.000" expanded="true" height="60" name="Tokenize (2)" width="90" x="45" y="30"/>
<operator activated="true" class="text:stem_snowball" compatibility="5.1.000" expanded="true" height="60" name="Stem (2)" width="90" x="404" y="30">
<parameter key="language" value="German"/>
</operator>
<connect from_port="document" to_op="Tokenize (2)" to_port="document"/>
<connect from_op="Tokenize (2)" from_port="document" to_op="Stem (2)" to_port="document"/>
<connect from_op="Stem (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="informationExtraction:tree_svm" compatibility="1.0.000" expanded="true" height="94" name="TreeSVM" width="90" x="305" y="396"/>
<operator activated="true" class="apply_model" compatibility="5.1.001" expanded="true" height="76" name="Apply Model" width="90" x="313" y="165">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="multiply" compatibility="5.1.001" expanded="true" height="94" name="Multiply" width="90" x="447" y="345"/>
<operator activated="true" class="select_attributes" compatibility="5.1.001" expanded="true" height="76" name="Select Attributes" width="90" x="514" y="165">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attributes" value="id|prediction(label)|sentence|confidence(neutral)|confidence(positiv)|confidence(negativ)"/>
<parameter key="include_special_attributes" value="true"/>
</operator>
<operator activated="true" class="write_database" compatibility="5.1.001" expanded="true" height="60" name="Write Database" width="90" x="648" y="165">
<parameter key="connection" value="192.168.1.124 @sentiment_analysis"/>
<parameter key="table_name" value="sentiment_unclassified_set_subjectivity"/>
<parameter key="overwrite_mode" value="overwrite"/>
</operator>
<connect from_op="JPW_getTestData" from_port="output" to_op="Process Documents from Data (2)" to_port="example set"/>
<connect from_op="Model SVM#" from_port="output" to_op="Apply Model" to_port="model"/>
<connect from_op="Process Documents from Data (2)" from_port="example set" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Multiply" to_port="input"/>
<connect from_op="Multiply" from_port="output 1" to_op="Select Attributes" to_port="example set input"/>
<connect from_op="Multiply" from_port="output 2" to_port="result 1"/>
<connect from_op="Select Attributes" from_port="example set output" to_op="Write Database" to_port="input"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
</process>[/tt]
Tagged:
0
Answers
In my own work I've found that SVMs are very sensitive to their parameters, kernel type, C and epsilon. Things aren't made any easier by by the fact that those parameters are rather tricky to define http://www.svms.org/parameters/; so your best bet is to do the combinations and check the performances.
I only mention this because I see that your model is made on one pass with C set to zero; without checking against the data it is not possible to be definitive, but I'm not that surprised that you just get one class in the prediction column. There is a handy paper on this subject at http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf which will point you in the right direction.
Hope so, have fun!