The significance of online product reviews has become indispensable for customers in making informed buying decisions, while e-commerce platforms use them to fine tune their recommender systems. However, since review writing is purely a voluntary process without any incentives, most customers opt out from writing reviews or write poor-quality ones. This lack of engagement poses credibility issues as fake or biased reviews can mislead buyers who rely on them for informed decision-making. To address this issue, this paper introduces a system that suggests product features and appropriate sentiment words to help users write informative product reviews in a structured manner. The system is based on Word2Vec model and Chi square test. The evaluation results demonstrates that the reviews with recommendations showed a 2 fold improvement both, in the quality of the features covered and correct usage of sentiment words, as well as a 19% improvement in overall usefulness compared to reviews without recommendations. Keywords: Word2Vec, Chi-square, Sentiment words, Product Aspect/Feature.