Putting data into tables and looping over it

Hi guys,

I am trying to run over a set of structured headlines and detect the issuer written in the headline.

I managed to write a code that will detect the issuer using a dictionary, I just need a little help on putting the data together systematically and looping over it efficiently.

Here's the code:

Import text to match an issuer to

document1 = "Analyst Actions: Citigroup Cuts Fiverr International's Price Target to $22 From $31, Reiterates Neutral Rating"
document2 = "Analyst Actions: JPMorgan Downgrades Beyond Meat to Neutral From Overweight, Cuts Price Target to $134 From $138"
document3 = "Analyst Actions: Credit Suisse Raises Beyond Meat's Price Target to $125 From $115; Keeps Neutral Rating"
document4 = "Analyst Actions: Oppenheimer Initiates Coverage on Beyond Meat With Perform Rating"
document5 = "Analyst Actions: Morgan Stanley Trims GoPro's Price Target to $3.50 From $4.50, Maintains Underweight Rating"
document6 = "GoPro downgraded by Zacks Investment Research to strong sell. $3.75 PT. (NASDAQ:GPRO)"

data = table(document1,document2,document3,document4,document5,document6)
data(:,2) % reference the 2nd column of data
data{:,2} % takes the content of the 2nd column of data

List of issuers

issuer = ["JPMorgan";
    "Citigroup";
    "Credit Suisse";
    "Oppenheimer";
    "Morgan Stanley";
    "Zacks";
    "UBS"]

Loop to find the issuer

for i = 1:size(issuer)
    
    if (contains(document6,issuer(i)))
        x = issuer(i)
    else
        nomatch = "No match."
    end
    
end

I basically would want to put the data in a table and instead of document6 in the loop replace it with the corresponding document (headline) to scan.

In the end I need to collect the write answers (x) and put it next to the original headline as final data.

Would appreciate your help.

Thank you!

Answer 1

[kgrunden]

If the table is reoriented to have the documents in a single row, instead each in a column, it possible to iterate the table and place the answer another column.

doc = {document1, document2, document3, document4, document5, document6}';
data = table(doc); 
for j = 1:size(doc)
     for i = 1:size(issuer)
          if (contains(doc{j}, issuer(i)))
              issuerCol(j) = issuer(i);
              break;
          else
              issuerCol(j) = "No match.";
          end
     end
end

data.issuer = issuerCol'

Executing the code then gives:

data =

  6×2 table

                                                             doc                                                                   issuer     
    ______________________________________________________________________________________________________________________    ________________

    {["Analyst Actions: Citigroup Cuts Fiverr International's Price Target to $22 From $31, Reiterates Neutral Rating"  ]}    "Citigroup"     
    {["Analyst Actions: JPMorgan Downgrades Beyond Meat to Neutral From Overweight, Cuts Price Target to $134 From $138"]}    "JPMorgan"      
    {["Analyst Actions: Credit Suisse Raises Beyond Meat's Price Target to $125 From $115; Keeps Neutral Rating"        ]}    "Credit Suisse" 
    {["Analyst Actions: Oppenheimer Initiates Coverage on Beyond Meat With Perform Rating"                              ]}    "Oppenheimer"   
    {["Analyst Actions: Morgan Stanley Trims GoPro's Price Target to $3.50 From $4.50, Maintains Underweight Rating"    ]}    "Morgan Stanley"
    {["GoPro downgraded by Zacks Investment Research to strong sell. $3.75 PT. (NASDAQ:GPRO)"                           ]}    "Zacks"         
    
>> data{3,:}

ans = 

  1×2 string array

    "Analyst Actions: Credit Suisse Raises Beyond Meat's Price Tar?"    "Credit Suisse"