Purpose The purpose of this paper is to determine the triggers of positive electronic word of mouth (eWOM) using real-time Big Data obtained from online retail sites/dedicated review sites. Design/methodology/approach In this study, real-time Big Data has been used and analysed through support vector machine, to segregate positive and negative eWOM. Thereafter, using natural language processing algorithms, this study has classified the triggers of positive eWOM based on their relative importance across six product categories. Findings The most important triggers of positive eWOM (like product experience, product type, product characteristics) were similar across different product categories. The second-level antecedents of positive eWOM included the person(s) for whom the product is purchased, the price and the source of the product, packaging and eagerness in patronising a brand. Practical implications The findings of this study indicate that the marketers who are active in the digital forum should encourage and incentivise their satisfied consumers to disseminate positive eWOM. Consumers with special interest for any product type (mothers or doctors for baby food) may be incentivised to write positive eWOM about the product’s ingredients/characteristics. Companies can launch the sequels of existing television or online advertisements addressing “for whom the product is purchased”. Originality/value This study identified the triggers of the positive eWOM using real-time Big Data extracted from online purchase platforms. This study also contributes to the literature by identifying the levels of triggers that are most, more and moderately important to the customers for writing positive reviews online.

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