Abstract
<p>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. </p><p>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. </p><p class="Els-Abstract-text">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 pre-processing 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.</p>
Highlights
With the rise and dominance of social media in almost every day-life activity, the need to understand its power and the potential it can offer has become more pressing
Through user-generated content, which is enabled by social media, opinion mining becomes critical in order to investigate the provided content to identify feedback towards a certain product, political campaign, initiative ...etc
This paper is organized as follows; section two starts by reviewing the literature to introduce different approaches used in sentiment analysis, and gives a brief overview of machine learning and Lexicon-based approaches and discusses two recent techniques employed on social media platforms which are localized twitter opinion mining using sentiment analysis and unsupervised sentiment analysis in social media
Summary
With the rise and dominance of social media in almost every day-life activity, the need to understand its power and the potential it can offer has become more pressing. This paper focuses on Instagram and the effect of the pictures that major fashion houses on the actual buying decisions of the customers. This would entail extracting the features of the object in question (whether a comment or an image) and further determining whether the opinion is positive or negative. This paper is organized as follows; section two starts by reviewing the literature to introduce different approaches used in sentiment analysis, and gives a brief overview of machine learning and Lexicon-based approaches and discusses two recent techniques employed on social media platforms which are localized twitter opinion mining using sentiment analysis and unsupervised sentiment analysis in social media. This paper is the first of a series of papers tackling sentiment analysis in Instagram
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