Learning word representations from large available corpora relies on the distributional hypothesis that words present in similar contexts tend to have similar meanings. Recent work has shown that word representations learnt in this manner lack sentiment information which, fortunately, can be leveraged using external knowledge. Our work addresses the question: Can affect lexica improve the word representations learnt from a corpus ? In this work, we propose techniques to incorporate affect lexica, which capture fine-grained information about a word's psycholinguistic and emotional orientation, into the training process of Word2Vec SkipGram, Word2Vec CBOW, and GloVe methods using a Joint Learning approach. We use affect scores from Warriner's affect lexicon to regularize the vector representations learnt from an unlabeled corpus. Our proposed method outperforms previously proposed methods on standard tasks for word similarity detection, outlier detection, and sentiment detection. We also show the usefulness of our approach for a new task related to the prediction of formality, frustration, and politeness in corporate communication.
This paper proposes integrating information from a semantic resource that quantifies the affect of different words into a text-based word embedding algorithm. 
The affect lexical seems to be a very interesting resource (although I'm not sure what it means to call it 'state of the art'), and definitely support the endeavour to make language models more reflective of complex semantic and pragmatic phenomena such as affect and sentiment. 
The justification for why we might want to do this with word embeddings in the manner proposed seems a little unconvincing to me:
- The statement that 'delighted' and 'disappointed' will have similar contexts is not evident to me at least (other then them both being participle / adjectives).
- Affect in language seems to me to be a very contextual phenomenon. Only a tiny subset of words have intrinsic and context-free affect. Most affect seems to me to come from the use of words in (phrasal, and extra-linguistic) contexts, so a more context-dependent model, in which affect is computed over phrases or sentences, would seem to be more appropriate. Consider words like 'expensive', 'wicked', 'elimination'...
The model proposes several applications (sentiment prediction, predicting email tone, word similarity) where the affect-based embeddings yield small improvements. However, in different cases, taking different flavours of affect information (V, A or D) produces the best score, so it is not clear what to conclude about what sort of information is most useful. 
It is not surprising to me that an algorithm that uses both WordNet and running text to compute word similarity scores improves over one that uses just running text. It also not surprising that adding information about affect improves the ability to predict sentiment and the tone of emails. 
To understand the importance of the proposed algorithm (rather than just the addition of additional data), I would like to see comparison with various different post-processing techniques using WordNet and the affect lexicon (i.e. not just Bollelaga et al.) including some much simpler baselines. For instance, what about averaging WordNet path-based distance metrics and distance in word embedding space (for word similarity), and other ways of applying the affect data to email tone prediction?