Sentiment analysis based on frequency of color names on social media

Abstract

This study explores the temporal changes in sentiment associated with eight color names over an 18-month period at four observation points. We focus on the valence aspect of sentiment. We collected four datasets, each separated by 6 months, and each containing 18 000 mentions of each of the eight color names in English from Twitter users around the world. We calculated the weighted average sentiment score of each instance when a color is mentioned.We find that purple and pink are the most positive in average sentiment score in all observation points, whereas brown, red, and orange are ranked as the lowest in average sentiment score. In terms of relative rank in sentiment value associated with the color names, we find the three consecutive datasets of July 2020, January 2021 and July 2021 are more consistent with one another, while the January 2022 dataset is more different from the earlier three datasets. This finding indicates that the temporal consistency in color-associated sentiment might maintain within 1 year, while evolve and show more difference in a longer timeline. This study is useful to marketing professionals by revealing that color names are associated with sentiment and that these associations can be monitored using social media data regularly. We suggest that marketers can use our method to analyse the color-associated sentiment of color names regularly, maybe on an annual basis, in order to choose color names wisely.

Have color representations in books changed over the past 200 years? An empirical analysis based on the Google Books Ngram corpus

“…Our study contributes to color research by extending our understanding of color‐emotion trend patterns over a long‐term period. Prior studies have examined color‐associated sentiments within a relatively short period 51 . Our study, based on a sizeable 200‐year data set, quantitatively depicts the color history and development process of color psychology and culture.…”

“…The word embedding method is the state‐of‐art approach in the computational text mining field, in which the meaning of the word is represented in a real‐valued vector in such a way that words that are closer in the vector space are expected to be similar in meaning 49 . In recent years, computational text mining approaches have been gradually applied in color emotion research, for example, examining the gender bias for English color terms, 15 clustering color emotion categories, 50 exploring the sentiment of color terms on social media 51 and representing color vector using multimodel approaches 52 . With the significant advantage of a long‐time and big data set, text‐mining approaches could flourish the color emotion research in the language usage and historical changing trend dimensions.…”

“…Prior studies have examined colorassociated sentiments within a relatively short period. 51 Our study, based on a sizeable 200-year data set, quantitatively depicts the color history and development process of color psychology and culture. Using a large data set of books, our study suggests that it is possible to explore multiplatform big data to analyze color-related phenomena using computational techniques.…”