Understanding the sentiments evoked by advertisements is crucial in serving them appropriately to consumers. Advertisements often use images to evoke sentiments. An image can convey multiple sentiments of different nature. Automatically predicting these multiple sentiments can help serve better advertisements to consumers, especially in an online scenario at scale. In this paper, we present a neural network model based on graph convolution to predict such sentiments, which exploits the semantic relationship among the sentiment labels. We use it to predict multiple sentiment labels using an annotated dataset of 30,340 image-based advertisements. We also find a distance metric that best represents the distribution of sentiments in the dataset and utilises it in a loss function that separates applicable sentiments from the non-applicable ones. We report an improvement in mean average precision and overall F1 score over a multi-modal multi-task state-of-the-art model.

Investigating brand perception is fundamental to marketing strategies. In this regard, brand image, defined by a set of attributes (Aaker, 1997), is recognized as a key element in indicating how a brand is perceived by various stakeholders such as consumers and competitors. Traditional approaches (e.g., surveys) to monitor brand perceptions are time-consuming and inefficient. In the era of digital marketing, both brand managers and consumers engage with a vast amount of digital marketing content. The exponential growth of digital content has propelled the emergence of pre-trained language models such as BERT and GPT as essential tools in solving myriads of challenges with textual data. This paper seeks to investigate the extent of brand perceptions (i.e., brand and image attribute associations) these language models encode. We believe that any kind of bias for a brand and attribute pair may influence customer-centric downstream tasks such as recommender systems, sentiment analysis, and question-answering, e.g., suggesting a specific brand consistently when queried for innovative products. We use synthetic data and real-life data and report comparison results for five contextual LMs, viz. BERT, RoBERTa, DistilBERT, ALBERT and BART.

Heuristics and cognitive biases are an integral part of human decision-making. Automatically detecting a particular cognitive bias could enable intelligent tools to provide better decision support. Detecting the presence of a cognitive bias requires a hand-crafted experiment and human interpretation. Our research aims to explore conversational agents as an effective tool to measure various cognitive biases in different domains. Our proposed conversational agent incorporates a bias measurement mechanism that is informed by the existing experimental designs and various experimental tasks identified in the literature. Our initial experiments to measure framing and loss-aversion biases indicate that conversational agents can be effectively used to measure the biases.