Dear Community,
I currently conduct a Sentiment Analysis for news media on a specific company.
I already matched each article ("row") with a specific sentiment score (+1 for each positive word contained, -1 for each negative word contained, so e.g. one article has a positivity score of 5 and a negativity score of -3).
Now I want to construct a sentiment measure:
The dominant sentiment of the article tells if I consider the entire text as positive/negative. So in the example above, my article is a positive article and therefore a +1 in total (another article may have more negative than positive words and is considered negative by -1).
One possibility for my sentiment measure is then #negative articles/#articles or #positive articles/#articles in a specific timeframe, e.g. in one week. The date of each article is contained in the news-media-source.
This is what my process looks like until now:
<?xml version="1.0" encoding="UTF-8"?><process version="9.3.000">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="9.3.000" expanded="true" name="Process">
<parameter key="logverbosity" value="init"/>
<parameter key="random_seed" value="2001"/>
<parameter key="send_mail" value="never"/>
<parameter key="notification_email" value=""/>
<parameter key="process_duration_for_mail" value="30"/>
<parameter key="encoding" value="SYSTEM"/>
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="9.3.000" expanded="true" height="68" name="Retrieve" width="90" x="45" y="34">
<parameter key="repository_entry" value="../Data/Finanzen.net"/>
</operator>
<operator activated="true" class="nominal_to_text" compatibility="9.3.000" expanded="true" height="82" name="Nominal to Text" width="90" x="179" y="34">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="Titel"/>
<parameter key="attributes" value=""/>
<parameter key="use_except_expression" value="false"/>
<parameter key="value_type" value="nominal"/>
<parameter key="use_value_type_exception" value="false"/>
<parameter key="except_value_type" value="file_path"/>
<parameter key="block_type" value="single_value"/>
<parameter key="use_block_type_exception" value="false"/>
<parameter key="except_block_type" value="single_value"/>
<parameter key="invert_selection" value="false"/>
<parameter key="include_special_attributes" value="false"/>
</operator>
<operator activated="true" class="text:data_to_documents" compatibility="8.2.000" expanded="true" height="68" name="Data to Documents" width="90" x="313" y="34">
<parameter key="select_attributes_and_weights" value="false"/>
<list key="specify_weights"/>
</operator>
<operator activated="true" class="loop_collection" compatibility="9.3.000" expanded="true" height="82" name="Loop Collection" width="90" x="447" y="34">
<parameter key="set_iteration_macro" value="false"/>
<parameter key="macro_name" value="iteration"/>
<parameter key="macro_start_value" value="1"/>
<parameter key="unfold" value="false"/>
<process expanded="true">
<operator activated="true" class="text:tokenize" compatibility="8.2.000" expanded="true" height="68" name="Tokenize (2)" width="90" x="45" y="34">
<parameter key="mode" value="non letters"/>
<parameter key="characters" value=".:"/>
<parameter key="language" value="English"/>
<parameter key="max_token_length" value="3"/>
</operator>
<operator activated="true" class="text:transform_cases" compatibility="8.2.000" expanded="true" height="68" name="Transform Cases (2)" width="90" x="179" y="34">
<parameter key="transform_to" value="lower case"/>
</operator>
<operator activated="true" class="text:filter_stopwords_german" compatibility="8.2.000" expanded="true" height="68" name="Filter Stopwords (2)" width="90" x="313" y="34">
<parameter key="stop_word_list" value="Standard"/>
</operator>
<operator activated="true" class="text:filter_by_length" compatibility="8.2.000" expanded="true" height="68" name="Filter Tokens (2)" width="90" x="514" y="34">
<parameter key="min_chars" value="3"/>
<parameter key="max_chars" value="10000"/>
</operator>
<connect from_port="single" to_op="Tokenize (2)" to_port="document"/>
<connect from_op="Tokenize (2)" from_port="document" to_op="Transform Cases (2)" to_port="document"/>
<connect from_op="Transform Cases (2)" from_port="document" to_op="Filter Stopwords (2)" to_port="document"/>
<connect from_op="Filter Stopwords (2)" from_port="document" to_op="Filter Tokens (2)" to_port="document"/>
<connect from_op="Filter Tokens (2)" from_port="document" to_port="output 1"/>
<portSpacing port="source_single" spacing="0"/>
<portSpacing port="sink_output 1" spacing="0"/>
<portSpacing port="sink_output 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="retrieve" compatibility="9.3.000" expanded="true" height="68" name="Retrieve (2)" width="90" x="45" y="289">
<parameter key="repository_entry" value="../Data/GRESD"/>
</operator>
<operator activated="true" class="retrieve" compatibility="9.3.000" expanded="true" height="68" name="Retrieve (3)" width="90" x="45" y="391">
<parameter key="repository_entry" value="../Data/Negationsliste"/>
</operator>
<operator activated="true" class="operator_toolbox:dictionary_sentiment_learner" compatibility="2.0.001" expanded="true" height="82" name="Dictionary-Based Sentiment (Documents)" width="90" x="246" y="289">
<parameter key="value_attribute" value="Klassifizierung"/>
<parameter key="key_attribute" value="Wort"/>
<parameter key="negation_attribute" value="Negationen"/>
<parameter key="negation_window_size" value="5"/>
<parameter key="use_symmetric_negation_window" value="true"/>
</operator>
<operator activated="true" class="operator_toolbox:apply_model_documents" compatibility="2.0.001" expanded="true" height="103" name="Apply Model (Documents)" width="90" x="715" y="289">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="write_excel" compatibility="9.3.000" expanded="true" height="103" name="Write Excel" width="90" x="876" y="136">
<parameter key="excel_file" value="D:\Franziska C. Weis\Masterarbeit\03 Datenanalyse\Rapid_Miner_Analysis.xlsx"/>
<parameter key="file_format" value="xlsx"/>
<enumeration key="sheet_names"/>
<parameter key="sheet_name" value="RapidMiner Data"/>
<parameter key="date_format" value="yyyy-MM-dd HH:mm:ss"/>
<parameter key="number_format" value="#.0"/>
<parameter key="encoding" value="SYSTEM"/>
</operator>
<connect from_op="Retrieve" from_port="output" to_op="Nominal to Text" to_port="example set input"/>
<connect from_op="Nominal to Text" from_port="example set output" to_op="Data to Documents" to_port="example set"/>
<connect from_op="Data to Documents" from_port="documents" to_op="Loop Collection" to_port="collection"/>
<connect from_op="Loop Collection" from_port="output 1" to_op="Apply Model (Documents)" to_port="doc"/>
<connect from_op="Retrieve (2)" from_port="output" to_op="Dictionary-Based Sentiment (Documents)" to_port="exa"/>
<connect from_op="Retrieve (3)" from_port="output" to_op="Dictionary-Based Sentiment (Documents)" to_port="neg"/>
<connect from_op="Dictionary-Based Sentiment (Documents)" from_port="mod" to_op="Apply Model (Documents)" to_port="mod"/>
<connect from_op="Apply Model (Documents)" from_port="exa" to_op="Write Excel" to_port="input"/>
<connect from_op="Write Excel" from_port="through" 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 attached the original source, where the date column is included.
With which operator can I create a sentiment measure, to perform a vector autoregression with this sentiment measure and a share price later on?
Thanks already for your answers!
Franziska
