Fixes #62 - Personality insights fully abstracted - readme updated

Author: mediumTaj

README.md

@@ -18,6 +18,7 @@ Use this SDK to build Watson-powered applications in Unity. It comes with a set
   * [Tradeoff Analytics](#tradeoff-analytics)
   * [Conversation](#conversation)
   * [Visual Recognition](#visual-recognition)
+  * [Personality Insights](#personality-insights)
 * [Developing a basic application in one minute](#developing-a-basic-application-in-one-minute)
 * [Documentation](#documentation)
 * [License](#license)
@@ -712,6 +713,70 @@ private void OnRecognizeText(TextRecogTopLevelMultiple multipleImages)
     }
 }
 ```
+### Personality Insights
+The IBM Watson™ [Personality Insights][personality_insights] service enables applications to derive insights from social media, enterprise data, or other digital communications. The service uses linguistic analytics to infer individuals' intrinsic personality characteristics, including Big Five, Needs, and Values, from digital communications such as email, text messages, tweets, and forum posts. The service can automatically infer, from potentially noisy social media, portraits of individuals that reflect their personality characteristics. 
+
+```cs
+PersonalityInsights m_personalityInsights = new PersonalityInsights();
+
+void Start () {
+	string dataPath = Application.dataPath + "/Watson/Examples/ServiceExamples/TestData/personalityInsights.json";
+	
+	if(!m_personalityInsights.GetProfile(OnGetProfile, dataPath, DataModels.ContentType.TEXT_PLAIN, DataModels.Language.ENGLISH))
+            Log.Debug("ExamplePersonalityInsights", "Failed to get profile!");
+}
+
+private void OnGetProfile(DataModels.Profile profile, string data)
+{
+    Log.Debug("ExamplePersonalityInsights", "data: {0}", data);
+    if(profile != null)
+    {
+        if(!string.IsNullOrEmpty(profile.id))
+            Log.Debug("ExamplePersonalityInsights", "id: {0}", profile.id);
+        if(!string.IsNullOrEmpty(profile.source))
+            Log.Debug("ExamplePersonalityInsights", "source: {0}", profile.source);
+        if(!string.IsNullOrEmpty(profile.processed_lang))
+            Log.Debug("ExamplePersonalityInsights", "proccessed_lang: {0}", profile.processed_lang);
+        if(!string.IsNullOrEmpty(profile.word_count))
+            Log.Debug("ExamplePersonalityInsights", "word_count: {0}", profile.word_count);
+        if(!string.IsNullOrEmpty(profile.word_count_message))
+            Log.Debug("ExamplePersonalityInsights", "word_count_message: {0}", profile.word_count_message);
+
+        if(profile.tree != null)
+        {
+            LogTraitTree(profile.tree);
+        }
+    }
+    else
+    {
+        Log.Debug("ExamplePersonalityInsights", "Failed to get profile!");
+    }
+}
+
+private void LogTraitTree(DataModels.TraitTreeNode traitTreeNode)
+{
+    if(!string.IsNullOrEmpty(traitTreeNode.id))
+        Log.Debug("ExamplePersonalityInsights", "id: {0}", traitTreeNode.id);
+    if(!string.IsNullOrEmpty(traitTreeNode.name))
+        Log.Debug("ExamplePersonalityInsights", "name: {0}", traitTreeNode.name);
+    if(!string.IsNullOrEmpty(traitTreeNode.category))
+        Log.Debug("ExamplePersonalityInsights", "category: {0}", traitTreeNode.category);
+    if(!string.IsNullOrEmpty(traitTreeNode.percentage))
+        Log.Debug("ExamplePersonalityInsights", "percentage: {0}", traitTreeNode.percentage);
+    if(!string.IsNullOrEmpty(traitTreeNode.sampling_error))
+        Log.Debug("ExamplePersonalityInsights", "sampling_error: {0}", traitTreeNode.sampling_error);
+    if(!string.IsNullOrEmpty(traitTreeNode.raw_score))
+        Log.Debug("ExamplePersonalityInsights", "raw_score: {0}", traitTreeNode.raw_score);
+    if(!string.IsNullOrEmpty(traitTreeNode.raw_sampling_error))
+        Log.Debug("ExamplePersonalityInsights", "raw_sampling_error: {0}", traitTreeNode.raw_sampling_error);
+    if(traitTreeNode.children != null && traitTreeNode.children.Length > 0)
+        foreach(DataModels.TraitTreeNode childNode in traitTreeNode.children)
+            LogTraitTree(childNode);
+}
+```
+
+
+
 
 ## Developing a basic application in one minute
 You can quickly develop a basic application that uses the Speech to Text service and the Natural Language Classifier service by using the prefabs that come with the SDK. Ensure that you prepare the test data before you complete the the following steps:
@@ -762,3 +827,4 @@ See [CONTRIBUTING.md](.github/CONTRIBUTING.md).
 [tradeoff_analytics]: http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/doc/tradeoff-analytics/
 [conversation]:http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/doc/conversation/
 [visual_recognition]: http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/visual-recognition/api/v3/
+[personality_insights]: http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/personality-insights/api/v2/