Our Sentimental LIAR dataset is a modified and further extended version of the LIAR extension introduced by Kirilin et al. In our dataset, the multi-class labeling of LIAR is converted to a binary annotation by changing half-true, false, barely-true and pants-fire labels to False, and the remaining labels to True. Furthermore, we convert the speaker names to numerical IDs in order to avoid bias with regards to the textual representation of names. The binary-label dataset is then extended by adding sentiments derived using the Google NLP API . Sentiment analysis determines the overall attitude of the text (i.e., whether it is positive or negative), and is quantified by a numerical score. If the sentiment score is positive, then we assign Positive for the sentiment attribute, otherwise Negative is assigned. We also introduced a further extension by adding emotion scores extracted using the IBM NLP API for each claim, which determine the detected level of 6 emotional states namely anger, sadness, disgust, fear and joy. The score for each emotion is between the range of 0 and 1. Table I demonstrates a sample record in Sentimental LIAR for a short claim in the LIAR dataset This repository contains the dataset for this paper: https://arxiv.org/abs/2009.01047