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public News ( String Query, String Options, String Market, String Adult, Double Latitude, Double Longitude, String NewsLocationOverride, String NewsCategory, String NewsSortBy ) : DataServiceQuery |
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| Query | String | Bing search query Sample Values : xbox |
| Options | String | Specifies options for this request for all Sources. Valid values are: DisableLocationDetection, EnableHighlighting. Sample Values : EnableHighlighting |
| Market | String | Market. Note: Not all Sources support all markets. Sample Values : en-US |
| Adult | String | Adult setting is used for filtering sexually explicit content Sample Values : Moderate |
| Latitude | Double | Latitude Sample Values : 47.603450 |
| Longitude | Double | Longitude Sample Values : -122.329696 |
| NewsLocationOverride | String | Overrides Bing location detection. This parameter is only applicable in en-US market. Sample Values : US.WA |
| NewsCategory | String | The category of news for which to provide results Sample Values : rt_Business |
| NewsSortBy | String | The sort order of results returned Sample Values : Date |
| return | DataServiceQuery |
|
public List<BingResult> GetBingNewsResult(string parameter)
{
//const string bingKey = "9F3eqLyfmNP0PInNOmS13FdSlPoajoJqLvrbvndqZFM";
const string bingKey = "wHln8JsLTWNIEDHZTxU3mTjnIr9T7HCGZPZ86BPYZKg";
var bing = new BingSearchContainer(
new Uri("https://api.datamarket.azure.com/Bing/Search/"))
{ Credentials = new NetworkCredential(bingKey, bingKey) };
var query = bing.News(parameter, null, "en-US", null, null, null, null, "rt_Business","Date");
var results = query.Execute();
List<BingResult> binglist = new List<BingResult> ();
foreach (var result in results)
{
//if (!string.IsNullOrEmpty (result.Url)) {
BingResult bingResult = new BingResult ();
bingResult.ID = result.ID;
bingResult.Url = result.Url;
bingResult.Description = result.Description;
bingResult.Date = result.Date;
bingResult.Title = result.Title;
binglist.Add (bingResult);
//}
}
return binglist;
}
private static List<NewsResult> GetNewArticles()
{
var client = new BingSearchContainer(
new Uri("https://api.datamarket.azure.com/Bing/search/")
);
client.Credentials = new NetworkCredential("accountKey", "5/397uT61db2oYMqva78buLleEq9XbhFhz9iuOX+c5U");
return client.News("Trump", null, "en-US", "Moderate", null, null, null, null, "Date").Execute().ToList();
}
static void updateArticlesForPlayer(string name, string id)
{
string topics = name;
string playerID = id;
int numOfArticles = 15;
string[] topicsArray = topics.Split('_');
string results = "";
List <string> resultTitles = new List <string>();
List <string> articleBodies = new List <string>();
List <string> urls = new List <string>();
try
{
string AccountKey = "W9xfglp9hpPxpxgoSsWqXzu7k6f+bqUA9Z+S7QHwwsg";
//For each user topic
foreach (string query in topicsArray)
{
// Root URL for bing search API
string rootUrl = "https://api.datamarket.azure.com/Bing/Search";
//Create a Bing Container per API specifications
var bingContainer = new Bing.BingSearchContainer(new Uri(rootUrl));
// The market location to use.
string location = "en-us";
// Add credentials
bingContainer.Credentials = new NetworkCredential(AccountKey, AccountKey);
// Create the search, getting top 5 results.
var newsQuery =
bingContainer.News(query, null, location, null, null, null, null, null, null);
newsQuery = newsQuery.AddQueryOption("$top", numOfArticles);
// Execute search.
var newsResults = newsQuery.Execute();
//Console.WriteLine("Number of articles: " + newsResults.Count());
//Add results to list.
foreach (var result in newsResults)
{
string article = getArticleBody(result.Url);
if (!article.Equals("ERROR"))
{
articleBodies.Add(article);
resultTitles.Add(result.Title);
urls.Add(result.Url);
}
else
{
numOfArticles--;
}
}
}
}
//Error running the query.
catch
{
results += "Error Processing Request. Please try again";
}
Console.WriteLine("Num of articles: {0}", numOfArticles);
Console.WriteLine("Num of article bodies: {0}", articleBodies.Count);
//Gets the unique words from each of the titles and stores them in a list
//Also get number of words in each title
List <string> words = new List <string>();
int[] numOfWords = new int[numOfArticles];
for (int i = 0; i < numOfArticles; i++)
{
string[] sArray = articleBodies[i].Split(' ');
numOfWords[i] = sArray.Length;
foreach (string s2 in sArray)
{
if (!words.Contains(s2))
{
words.Add(s2);
//Console.WriteLine(s2);
}
}
}
//Stores the frequency of the occurences of each word
int[,] frequencyArray = new int[numOfArticles, words.Count];
Array.Clear(frequencyArray, 0, frequencyArray.Length);
for (int i = 0; i < numOfArticles; i++)
{
string[] sArray = articleBodies[i].Split(' ');
foreach (string s2 in sArray)
{
//Location of the word in the array of words
int j = words.IndexOf(s2);
frequencyArray[i, j]++;
}
}
//Normalized Frequency Array
double[,] nFrequencyArray = new double[numOfArticles, words.Count];
for (int i = 0; i < numOfArticles; i++)
{
for (int j = 0; j < words.Count; j++)
{
nFrequencyArray[i, j] = ((double)frequencyArray[i, j]) / ((double)numOfWords[i]);
}
}
//Inverse Document Frequency
double[] idf = new double[words.Count];
for (int i = 0; i < words.Count; i++)
{
//How many documents the current word appears in
int appearances = 0;
for (int j = 0; j < numOfArticles; j++)
{
if (frequencyArray[j, i] > 0)
{
appearances++;
}
}
idf[i] = (double)(1 + Math.Log((double)numOfArticles / (double)appearances));
}
//TF-IDF
double[,] tfIDF = new double[numOfArticles, words.Count];
for (int i = 0; i < numOfArticles; i++)
{
for (int j = 0; j < words.Count; j++)
{
tfIDF[i, j] = idf[j] * nFrequencyArray[i, j];
//Console.WriteLine(tfIDF[i, j]);
}
}
//The location in the title list of each chosen article. The first one is always 0.
int[] selectedArticles = { -1, -1, -1, -1 };
//Calculate cosine similarity between all articles, to see which ones are the least similar
//int leastSimilarArticle = 0;
//double smallestAngle = 1;
double[,] cosineArray = new double[numOfArticles, numOfArticles];
for (int k = 0; k < numOfArticles; k++)
{
for (int i = 0; i < numOfArticles; i++)
{
double dotProduct = 0;
double length1 = 0;
double length2 = 0;
for (int j = 0; j < words.Count; j++)
{
dotProduct += (tfIDF[k, j] * tfIDF[i, j]);
length1 += Math.Pow(tfIDF[k, j], 2);
length2 += Math.Pow(tfIDF[i, j], 2);
}
double cosine = dotProduct / (Math.Pow(length1, .5) * Math.Pow(length2, .5));
cosineArray[k, i] = Math.Round(cosine, 6);
}
}
//Print out cosine array
System.IO.StreamWriter file = new System.IO.StreamWriter("C:\\Users\\Sumbhav\\Documents\\Thesis\\cosine.txt");
for (int i = 0; i < numOfArticles; i++)
{
for (int j = 0; j < numOfArticles; j++)
{
file.Write(cosineArray[j, i].ToString("0.000000") + "\t\t\t");
}
file.WriteLine();
}
file.WriteLine("\n");
//Get the average similarity of each article to the other articles
double[] averageSimilarity = new double[numOfArticles];
Array.Clear(averageSimilarity, 0, averageSimilarity.Length);
for (int i = 0; i < numOfArticles; i++)
{
for (int j = 0; j < numOfArticles; j++)
{
averageSimilarity[i] += cosineArray[j, i];
}
averageSimilarity[i] /= numOfArticles;
file.WriteLine(averageSimilarity[i]);
}
//Find the mean and standard deviation of the average similarities
double mean = averageSimilarity.Average();
double stDev = StandardDeviation(averageSimilarity);
//Current selected article
int selectedArticle = -1;
//Find and select the articles with a similarity score closest to -2, -1, 0, and 1 standard deviation(s) from the mean
for (int i = -2; i <= 1; i++)
{
double compareValue = mean + i * stDev;
//Distance between the compare value and the similarity score of the article being examined
double distance = 1.0;
for (int j = 0; j < numOfArticles; j++)
{
if (Math.Abs(averageSimilarity[j] - compareValue) < distance)
{
distance = Math.Abs(averageSimilarity[j] - compareValue);
selectedArticle = j;
}
}
//Add selected article to final selection of articles
selectedArticles[i + 2] = selectedArticle;
}
file.WriteLine();
foreach (int i in selectedArticles)
{
file.WriteLine(resultTitles[i]);
file.WriteLine(urls[i] + "\n");
}
file.WriteLine("\n");
foreach (string s in resultTitles)
{
file.WriteLine(s);
}
file.Close();
//MySql connection set up
MySqlConnectionStringBuilder conn_string = new MySqlConnectionStringBuilder();
conn_string.Server = "sethia.cosfzwdmywyo.us-west-2.rds.amazonaws.com";
conn_string.UserID = "cheetobuv10";
conn_string.Password = "******";
conn_string.Database = "NBAbase";
MySqlConnection l_DBConn = new MySqlConnection();
l_DBConn.ConnectionString = conn_string.ToString();
//Open connection to DB (already open based on where it's being called)
//l_DBConn.Open();
//Add articles to database
MySqlCommand command;
for (int i = 0; i < selectedArticles.Length; i++)
{
command = l_DBConn.CreateCommand();
command.Connection = l_DBConn;
command.CommandText = "replace into Articles SET playerID='" + playerID + "', articleNumber=" + (i + 1) + ", url='" + urls[selectedArticles[i]] + "', title='" + resultTitles[selectedArticles[i]] + "'";
command.ExecuteNonQuery();
}
//Close connection to DB
l_DBConn.Close();
Console.WriteLine("Database updated with articles");
Console.ReadLine();
}
public List<NewsResult> Query(string company)
{
string market = "en-us";
string options = null;
string adult = null;
double? lat = null;
double? lon = null;
string newLocation = null;
string newsCategory = "rt_Business";
string sortBy = "Date";
Container = new BingSearchContainer(new Uri(rootUrl));
Container.Credentials = new NetworkCredential(accountKey, accountKey);
var query = Container.News(company, options, market, adult, lat, lon, newLocation, newsCategory, sortBy);
List<NewsResult> totalSearchList = new List<NewsResult>();
for (int i = 0; i < 4; i++)
{
int skip = totalSearchList.Count();
var offsetQuery = query.AddQueryOption("$skip", skip.ToString());
var result = offsetQuery.Execute().ToList();
totalSearchList.AddRange(result);
}
return totalSearchList;
}
