Question: Is the analysis of human affect from text an important topic in natural language understanding?
Evidence: Affect is a term that subsumes emotion and longer term constructs such as mood and personality and refers to the experience of feeling or emotion BIBREF0 . BIBREF1 picard1997affective provides a detailed discussion of the importance of affect analysis in human communication and interaction. Within this context the analysis of human affect from text is an important topic in natural language understanding, examples of which include sentiment analysis from Twitter BIBREF2 , affect analysis from poetry BIBREF3 and studies of correlation between function words and social/psychological processes BIBREF4 . People exchange verbal messages which not only contain syntactic information, but also information conveying their mental and emotional states. Examples include the use of emotionally colored words (such as furious and joy) and swear words. The automated processing of affect in human verbal communication is of great importance to understanding spoken language systems, particularly for emerging applications such as dialogue systems and conversational agents.
Statistical language modeling is an integral component of speech recognition systems, with other applications such as machine translation and information retrieval. There has been a resurgence of research effort in recurrent neural networks for language modeling BIBREF5 , which have yielded performances far superior to baseline language models based on n-gram approaches. However, there has not been much effort in building neural language models of text that leverage affective information. Current literature on deep learning for language understanding focuses mainly on representations based on word semantics BIBREF6 , encoder-decoder models for sentence representations BIBREF7 , language modeling integrated with symbolic knowledge BIBREF8 and neural caption generation BIBREF9 , but to the best of our knowledge there has been no work on augmenting neural language modeling with affective information, or on data-driven approaches to generate emotional text.
Motivated by these advances in neural language modeling and affective analysis of text, in this paper we propose a model for representation and generation of emotional text, which we call the Affect-LM. Our model is trained on conversational speech corpora, common in language modeling for speech recognition applications BIBREF10 . Figure 1 provides an overview of our Affect-LM and its ability to generate emotionally colored conversational text in a number of affect categories with varying affect strengths. While these parameters can be manually tuned to generate conversational text, the affect category can also be automatically inferred from preceding context words. Specifically for model training, the affect category is derived from features generated using keyword spotting from a dictionary of emotional words, such as the LIWC (Linguistic Inquiry and Word Count) tool BIBREF11 . Our primary research questions in this paper are:
Q1:Can Affect-LM be used to generate affective sentences for a target emotion with varying degrees of affect strength through a customizable model parameter?
Q2:Are these generated sentences rated as emotionally expressive as well as grammatically correct in an extensive crowd-sourced perception experiment?
Q3:Does the automatic inference of affect category from the context words improve language modeling performance of the proposed Affect-LM over the baseline as measured by perplexity?
The remainder of this paper is organized as follows. In Section "Related Work" , we discuss prior work in the fields of neural language modeling, and generation of affective conversational text. In Section "LSTM Language Model" we describe the baseline LSTM model and our proposed Affect-LM model. Section "Experimental Setup" details the experimental setup, and in Section "Results" , we discuss results for customizable emotional text generation, perception studies for each affect category, and perplexity improvements over the baseline model before concluding the paper in Section "Conclusions and Future Work" .
Within this context the analysis of human affect from text is an important topic in natural language understanding, examples of which include sentiment analysis from Twitter, affect analysis from poetry and studies of correlation between function words and social/psychological processes.
Answer: Yes, it is.