Allennlp-Based Sentiment Analysis Model

This code defines a simple sentiment analysis model using components from the Allennlp library. It first defines a tokenizer, vocabulary, text field, label field, embedder, feedforward network, and regularizer. Then it creates an instance and trains a model. Finally, it defines a function that uses the model to predict the sentiment of an input sentence.

Technology Stack : Allennlp

Code Type : Text classification model

Code Difficulty : Intermediate

                
                    
import random
from allennlp.data import Instance
from allennlp.data.fields import TextField, LabelField
from allennlp.data.tokenizers import Tokenizer
from allennlp.data.vocabulary import Vocabulary
from allennlp.models import Model
from allennlp.modules import Embedder, FeedForward
from allennlp.nn import Regularizer
from allennlp.training import Trainer

def random_sentiment_analysis():
    # Define a tokenizer for the sentences
    tokenizer = Tokenizer()

    # Create a vocabulary with some sample tokens
    vocab = Vocabulary()
    vocab.add_token_to_index("hello", 1)
    vocab.add_token_to_index("world", 2)
    vocab.add_token_to_index("positive", 3)
    vocab.add_token_to_index("negative", 4)

    # Create a text field with a sample sentence
    sentence = "hello world"
    text_field = TextField(tokenizer.tokenize(sentence), vocab)

    # Create a label field with a sentiment
    label = "positive"
    label_field = LabelField(label, vocab)

    # Create an instance
    instance = Instance({"text": text_field, "label": label_field})

    # Define an embedder
    embedder = Embedder(vocab, 4, 10, dropout=0.2)

    # Define a feedforward network
    feedforward = FeedForward(10, 2, dropout=0.2)

    # Define a regularizer
    regularizer = Regularizer()

    # Define a model
    model = Model(
        embedding=embedder,
        feedforward=feedforward,
        regularizer=regularizer
    )

    # Create a trainer
    trainer = Trainer(model, regularizer, instance)

    # Train the model
    trainer.train()

def xxx(sentence, sentiment):
    # Tokenize the sentence
    tokens = tokenizer.tokenize(sentence)

    # Create a text field with the tokens
    text_field = TextField(tokens, vocab)

    # Create a label field with the sentiment
    label_field = LabelField(sentiment, vocab)

    # Create an instance
    instance = Instance({"text": text_field, "label": label_field})

    # Predict the sentiment
    prediction = model.forward(instance)

    # Return the predicted sentiment
    return prediction["label"]