A Sentiment Analysis Tool for Determining the Promotional Success of Fashion Images on Instagram

Sentiment Analysis (SA) or Opinion Mining is the process of analysing natural language texts to detect an emotion or a pattern of emotions towards a certain product to make a decision about that product. SA is a topic of text mining, Natural Language Processing (NLP) and web mining disciplines. Research in SA is currently at its peak given the amount of data generated from social media networks. The concept is that consumers are expressing exactly what they need, want and expect from a product but on the other hand the companies don’t have the tools to analyse and understand these feelings to satisfy these consumers accordingly. One of the applications that generate a high rate of reactions and sentiments in social networks is Instagram. This study focuses on analysing the reactions generated by the top 50 fashion houses on Instagram given their top 20 images with the highest number of likes. The approach taken in this study is to qualify the visual aesthetics of fashion images and to establish why some succeed on social media more than others. The basic question asked in this paper is whether there are certain visual aesthetics that appeal more to the user and are therefore more successful on social media than others as determined by a measure we introduce, ‘Social Value’. To do so, a sentiment analysis tool is developed to measure the proposed social value of each image. An input of comments from each image will be processed. Each comment will go through a preprocessing phase; each word will be placed through a lexicon to identify if it is positive or negative. The output of the lexicon is a score value assigned to each comment to identify its degree of positivity, negativity, or it has no effect on the social value. Adding to these results, the number of likes and shares would also be taken into consideration quantifying the image’s value. A cumulative result is then produced to determine the social value of an image.