Alright, let's get started by setting up Firebase. First things first, head over to the Firebase console (https://console.firebase.google.com/) and create a new project. Once that's done, you'll need to add an iOS or Android app to your project by filling in all the required details.

If you're using Android Studio, you'll need to download the google-services.json file and drop it into your project's app folder. For iOS, fire up XCode, download the GoogleService-Info.plist, and add it to your runner folder in your Flutter app.

Don't forget to include the Firebase core and Firebase ML Kit Natural Language dependencies in your build.gradle or Podfile.

Next up, let's get those Firebase ML Kit Natural Language dependencies into your code. Here's how you can do it:

// For Android
implementation 'com.google.firebase:firebase-ml-natural-language:22.0.1'
implementation 'com.google.firebase:firebase-ml-natural-language-language-id-model:20.0.7'

// For iOS
pod 'Firebase/MLNaturalLanguage'

Now, it's time to initialize Firebase. Depending on your platform, you'll do this in your MainActivity file (for Android) or AppDelegate (for iOS). Here's what it looks like:

// For Android
Firebase.initializeApp(this);

// For iOS
FirebaseApp.configure()

Okay, so here's the thing—Firebase ML Kit doesn't have a direct sentiment analysis API. But don't worry! You can use the on-device text recognition API to grab the text, and then use any cloud-based sentiment analysis API. Here's an example using the language API:

// Instantiate the Language Identification Model
FirebaseLanguageIdentification languageIdentifier = FirebaseNaturalLanguage.getInstance().getLanguageIdentification();

// Process the Text
languageIdentifier.identifyLanguage("your text here")
    .addOnSuccessListener(
      new OnSuccessListener<String>() {
        @Override
        public void onSuccess(@Nullable String languageCode) {
          if (!languageCode.equals("und")) {
            // Language detected successfully, perform your sentiment analysis here
          } else {
            // No Language was identified
          }
        }
    })
    .addOnFailureListener(
      new OnFailureListener() {
        @Override
        public void onFailure(@NonNull Exception e) {
          // Model couldn’t be loaded or other error.
        }
    });

Just a heads-up, this only identifies the language of the text. For actual sentiment analysis, you'll need to use an external NLP (Natural Language Processing) service or create your own custom models for more advanced stuff.