Analyzing the Brand Personality Perception in Social Networks: The Competence Dimension

Developing a distinct brand personality enables companies to differentiate themselves from competitors and effectively engage with customers. However, evaluating the customers’ brand personality perceptions is a challenge, as traditional methods are costly and not objective. In this study, we focus on the brand personality dimension of "competence" and leverage the social networks to analyze the perceptions of Persian customers. For this purpose, the comments written in the Persian language regarding the brands or users’ experiences with the brands are extracted from social networks. Then, the natural language processing techniques such as TF-IDF and word2vec are employed to prepare the data for developing machine learning models. These models categorize users' comments into three classes: aligned, non-aligned, and neutral. The classification depends on whether the comment is in line with the brand's competence, against it, or neutra. The k-nearest neighbors, Naive Bayes, Artificial Neural Network and long short-term memory (LSTM) are trained on the dataset. The results demonstrate that the LSTM model surpasses the performance of other models by achieving the f1-score of 93 percent. Finally, the LSTM model used to evaluate the customers' perception of Snowa's brand personality, as a case study.