Emotion-corpus guided lexicons for sentiment analysis on Twitter.

Research in Psychology have proposed frameworks that map emotion concepts with sentiment concepts. In this paper we study this mapping from a computational modelling perspective with a view to establish the role of an emotion-rich corpus for lexicon-based sentiment analysis. We propose two different methods which harness an emotion-labelled corpus of tweets to learn world-level numerical quantification of sentiment strengths over a positive to negative spectrum. The proposed methods model the emotion corpus using a generative unigram mixture model (UMM), combined with the emotion-sentiment mapping proposed in Psychology [6] for automated generation of sentiment lexicons. Sentiment analsysis experiments on benchmark Twitter data sets confirm the equality of our proposed lexicons. Further a comparative analysis with standard sentiment lexicons suggest that the proposed lexicons lead to a significantly better performance in both sentimentclassification and sentiment intensity prediction tasks

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation

A Generative Model of Group Conversation

Conversations with non-player characters (NPCs) in games are typically confined to dialogue between a human player and a virtual agent, where the conversation is initiated and controlled by the player. To create richer, more believable environments for players, we need conversational behavior to reflect initiative on the part of the NPCs, including conversations that include multiple NPCs who interact with one another as well as the player. We describe a generative computational model of group conversation between agents, an abstract simulation of discussion in a small group setting. We define conversational interactions in terms of rules for turn taking and interruption, as well as belief change, sentiment change, and emotional response, all of which are dependent on agent personality, context, and relationships. We evaluate our model using a parameterized expressive range analysis, observing correlations between simulation parameters and features of the resulting conversations. This analysis confirms, for example, that character personalities will predict how often they speak, and that heterogeneous groups of characters will generate more belief change.Comment: Accepted submission for the Workshop on Non-Player Characters and Social Believability in Games at FDG 201

Emotion-aware polarity lexicons for Twitter sentiment analysis.

Theoretical frameworks in psychology map the relationships between emotions and sentiments. In this paper we study the role of such mapping for computational emotion detection from text (e.g. social media) with a aim to understand the usefulness of an emotion-rich corpus of documents (e.g. tweets) to learn polarity lexicons for sentiment analysis. We propose two different methods that leverage a corpus of emotion-labelled tweets to learn word-polarity lexicons. The proposed methods model the emotion corpus using a generative unigram mixture model (UMM), combined with the emotion-sentiment mapping proposed in Psychology for automated generation of word-polarity lexicons that capture emotion-rich vocabulary. We comparatively evaluate the quality of the proposed mixture model in learning emotion-aware sentiment lexicons with those generated using supervised latent dirichlet allocation (sLDA) and word-document frequency (WDF) statistics. Sentiment analysis experiments on benchmark Twitter data sets confirm the quality of our proposed lexicons. Further a comparative analysis with sLDA, WDF based emotion-aware lexicons and standard sentiment lexicons that are agnostic to emotion knowledge suggest that the proposed lexicons lead to a significantly better performance in both sentiment classification and sentiment intensity prediction tasks

Semantic smoothing for Twitter sentiment analysis

Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifier from tweets data often faces the data sparsity problem partly due to the large variety of short forms introduced to tweets because of the 140-character limit. In this work we propose using semantic smoothing to alleviate the data sparseness problem. Our approach extracts semantically hidden concepts from the training documents and then incorporates these concepts as additional features for classifier training. We tested our approach using two different methods. One is shallow semantic smoothing where words are replaced with their corresponding semantic concepts; another is to interpolate the original unigram language model in the Naive Bayes NB classifier with the generative model of words given semantic concepts. Preliminary results show that with shallow semantic smoothing the vocabulary size has been reduced by 20%. Moreover, the interpolation method improves upon shallow semantic smoothing by over 5% in sentiment classification and slightly outperforms NB trained on unigrams only without semantic smoothing

Fine-grained Affective Processing Capabilities Emerging from Large Language Models

Large language models, in particular generative pre-trained transformers (GPTs), show impressive results on a wide variety of language-related tasks. In this paper, we explore ChatGPT's zero-shot ability to perform affective computing tasks using prompting alone. We show that ChatGPT a) performs meaningful sentiment analysis in the Valence, Arousal and Dominance dimensions, b) has meaningful emotion representations in terms of emotion categories and these affective dimensions, and c) can perform basic appraisal-based emotion elicitation of situations based on a prompt-based computational implementation of the OCC appraisal model. These findings are highly relevant: First, they show that the ability to solve complex affect processing tasks emerges from language-based token prediction trained on extensive data sets. Second, they show the potential of large language models for simulating, processing and analyzing human emotions, which has important implications for various applications such as sentiment analysis, socially interactive agents, and social robotics