Sentiment analysis is the process of determining the emotional tone of a piece of text, such as a review or a tweet. A Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) that can be used for sentiment analysis, and Keras is a high-level neural networks API that allows for easy and fast prototyping, and it is written in Python.

The first step in setting up a LSTM model for sentiment analysis using Keras is to prepare the dataset. For sentiment analysis, we typically use a dataset of text samples, such as movie reviews, along with their corresponding labels indicating whether the review is positive or negative. The dataset should be divided into a training set and a test set.

Next, we need to preprocess the text data. This involves cleaning the text by removing special characters, stop words, and punctuations. It also involves tokenizing the text into individual words and creating a vocabulary of all the unique words in the dataset. We can also convert the words to numerical values using one-hot encoding or word embeddings like word2vec or GloVe.

Once the text data is preprocessed, we can build the LSTM model. Keras provides a built-in function for creating a sequential model, which is the type of model used for LSTMs. We can add layers to this model, such as an input layer, an LSTM layer and an output layer. The input layer receives the preprocessed text data, the LSTM layer process the data in a sequential manner, capturing the context of the text and the output layer produces the sentiment prediction.

LSTM layers are particularly useful for working with sequential data such as text, speech and time-series data. They are different from the traditional RNN in the way that they have a memory cell that can keep track of information for longer periods of time, this allows the model to understand the meaning of the text even if it is in a long sentence.

After building the model, we need to compile it. This involves specifying the optimizer and loss function that will be used during training. The optimizer is responsible for updating the model’s weights during training, and the loss function is used to measure the model’s performance.

Finally, we can train the model on our dataset. This is done by feeding the model text samples from the training set and adjusting the weights based on the performance of the model. This process is repeated for a set number of iterations, known as epochs, until the model reaches a satisfactory level of performance.

Once the model is trained, we can use it to make predictions on new text samples. This can be done by calling the predict function on the model and passing in the text we want to classify.

In summary, setting up a LSTM (RNN) model for sentiment analysis using Keras involves preparing a dataset of text samples, preprocessing the text data, building the LSTM model, compiling the model, training it on the dataset, and making predictions with new text samples. LSTM layers are key element of the model, they allow the model to understand the meaning of the text based on the context in which the words appear and also help to keep track of information for longer periods of time.