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Agriculture census information is a leading source of a country's development. Such information is used by many who provide services to farmers and rural communities. The human interaction system provides a move from entity and object centric processing to relationship and event centric processing. The computer is interacted efficiently with the human for giving the solution to their problems. The integrated system gives the ability to extract, represent and reason about a variety of relationship as well as providing integral support. The proposed system is called farmer helping system which integrates relevant web services like soil information, plant disease information, and plant information and also contains pesticides and fungicides information. This farmer helping system gives the appropriate solution for farmers. The farmer helping system analyses the message from the user, contacts appropriate resources, and return actionable information, while requiring minimal involvement or technology consciousness from the user. The semantically annotated data is used for integration, search, analysis, discovery, question answering and situational awareness for making the user system efficient. This system can help for agricultural development planning and formulate of agricultural policies.

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Interpersonal conversation, or word-of-mouth (WOM), is one of the important factors in affecting product sales. WOM can not only increase product awareness among potential buyers but can also affect their buying decisions [8].With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information [1]. Due to large number of reviews for a single product, it is difficult for the customers to find the most useful reviews among such a large quantity and to find the true quality of the product. In this paper, we examine the reviews based on the textual characteristics in different time period to find the impact of reviews. To understand better the factors that influence consumers perception of usefulness and the factors that affect consumers the most, we conducted a two-level study. First, we performed an explanatory econometric analysis, trying to identify the aspects of a review that are important determinants of its usefulness and impact. Then, at the second level, we built a model using component weight assignment algorithm with SVM to pedict the most useful reviews.