Spreading of large unstructured information generated by social networks provide useful results to the effective recommender. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. This paper suggests an approach to identify product opportunities from customer reviews in social media. Our approach explores to identify multiple reviews and get processed by sentimental analysis. These extracted data may contain positive and negative sentimental reviews. The recommended product can be visible to all the users who are present in network. The sentimental words get ordered based on priority. With the help of this, the user can get the best product by comparing it with other products. Sentimental analysis plays an important role to decide a product and both the users and manufacturers get benefit through this.
Product Reviews, Sentimental Analysis, Recommended Product, Social Media Mining
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