چکیده:
Brand community interactions and online customer support have become major platforms of brand sentiment strengthening and loyalty creation. Rapid brand responses to each customer request though inbound tweets in twitter and taking proper actions to cover the needs of customers are the key elements of positive brand sentiment creation and product or service initiative management in the realm of intense competition. In this research, there has been an attempt to collect near three million tweets of inbound customer requests and outbound brand responses of international enterprises for the purpose of brand sentiment analysis. The steps of CRISP-DM have been chosen as the reference guide for business and data understanding, data preparation, text mining, validation of results as well as the final discussion and contribution. A rich phase of text pre-processing has been conducted and various algorithms of sentiment analysis were applied for the purpose of achieving the most significant analytical conclusions over the sentiment trends. The findings have shown that the sentiment of customers toward a brand is significantly correlated with the proper response of brands to the brand community over social media as well as providing the customers with a deep feeling of reciprocal understanding of their needs in a mid-to-long range planning.
خلاصه ماشینی:
Rapid brand responses to each customer request though inbound tweets in twitter and taking proper actions to cover the needs of customers are the key elements of positive brand sentiment creation and product or service initiative management in the realm of intense competition.
In this research, there has been an attempt to collect near three million tweets of inbound customer requests and outbound brand responses of international enterprises for the purpose of brand sentiment analysis.
Keywords: Brand community; Sentiment analysis; Text mining; Twitter; Customer support.
After applying and validating various algorithms over the final textual dataset, the outputs were analyzed and the brand sentiment that is impacted by the inbound customer tweets and outbound services of enterprises through response tweets are discussed in detail.
As provided in the literature, the online communication is majorly analyzed through text mining algorithms for different purposes, such as product planning (Jeong, Yoon, & Lee, 2017), marketing (AlAlwan, Rana, Dwivedi, & Algharabat, 2017; Kapoor et al.
Within the realm of text analytics, the sentiment analysis has emerged as one of the most interesting and useful sets of algorithms that are used to describe the sentiments of customers toward a brand through a series of communications and comments analysis over a social media.
Sentiment Analysis The growing ease and enthusiasm with which researchers and practitioners can access a variety of information sources over the web on the international markets is a strategic resource for brand management (Shirdastian, Laroche, & Richard, 2017).
, 2015, Mohamed Hussein, 2018; Tawunrat and Jeremy, 2015; Zhang, Zeng, Li, Wang, & Zuo, 2009).