Sentiment transfer involves changing the sentiment of a sentence, such as from a positive to negative sentiment, whilst maintaining the informational content. Whilst this challenge in the NLP research domain can be constructed as a translation problem, traditional sequence-to-sequence translation methods are inadequate due to the dearth of parallel corpora for sentiment transfer. Thus, sentiment transfer can be posed as an unsupervised learning problem where a model must learn to transfer from one sentiment to another in the absence of parallel sentences. Given that the sentiment of a sentence is often defined by a limited number of sentiment-specific words within the sentence, this problem can also be posed as a problem of identifying and altering sentiment-specific words as a means of transferring from one sentiment to another. In this dissertation we use a novel method of sentiment word identification from the interpretability literature called the method of rationales. This method identifies the words or phrases in a sentence that explain the ‘rationale' for a classifier's class prediction, in this case the sentiment of a sentence. This method is then compared against a baseline heuristic sentiment word identification method. We also experiment with a pretrained encoder-decoder Transformer model, known as BART, as a method for improving upon previous sentiment transfer results. This pretrained model is fine-tuned first in an unsupervised manner as a denoising autoencoder to reconstruct sentences where sentiment words have been masked out. This fine-tuned model then generates a parallel corpus which is used to further fine-tune the final stage of the model in a self-supervised manner. Results were compared against a baseline using automatic evaluations of accuracy and BLEU score as well as human evaluations of content preservation, sentiment accuracy and sentence fluency. The results of this dissertation show that both neural network and heuristic-based methods of sentiment word identification achieve similar results across models for similar levels of sentiment word removal for the Yelp dataset. However, the heuristic approach leads to improved results with the pretrained model on the Amazon dataset. We also find that using the pretrained Transformers model improves upon the results of using the baseline LSTM trained from scratch for the Yelp dataset for all automatic metrics. The pretrained BART model scores higher across all human-evaluated outputs for both datasets, which is likely due to its larger size and pretraining corpus. These results also show a similar trade-off between content preservation and sentiment transfer accuracy as in previous research, with more favourable results on the Yelp dataset relative to the baseline.