A wealth of unstructured opinion data exists online. Every day, millions of consumers add to this data when they share their opinion on a range of things, including feedback about their experiences with products and services. This feedback is volunteered, it contains the raw, unsolicited views and opinions about a brand, individual or event. Opinion Mining finds out the drivers behind the sentiment. By understanding what is driving the sentiment and how one is performing based on Net Sentiment, opinion data can be used to expose critical areas of strength and weakness. This data allows decision-makers in business, from customer experience and marketing to risk and compliance teams, to make the targeted, strategic overhauls needed to reinvigorate profitability or reclaim slipping market share. It is practically impossible to analyze all this reviews manually, so and automated aspect-based opinion mining approach is used. This paper focuses on aspect level, shows a comparative study amongst existing algorithm and proposes a new syntactic based approach which uses dictionary, aggregate score for opinion words. The dataset used was for restaurant reviews. The proposed method achieved a total accuracy of 87.06%.