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The growth of digital documents on web becomes the massive sources for online market analyzing at broad level. The study of market research over online incorporating new parameter called sentiment analysis. The sentiment analysis plays a crucial role for identifying behavior of customers by means of natural language processing from customer feedback about product or services. The opinion mining have done from the user data over web related activities such as search history, blog activities, forums, comments on the social network, express the opinion about the concept/product and suggestion or recommendations. The present system is non-adaptive relation identification system works on existing, predetermined set of relations and it cannot identify the new type relation for opinion mining. The existing system are also neglected the static sentiments of users. This paper proposed ontology based adaptive sentiment analysis system for extracting new features added on the user space. In our work, the ontology and 3D space clustering framework which allows incorporation of domain knowledge for predicting sentimental analysis via opinion mining.

Purpose The purpose of this paper aims to reduce the manpower, electricity, and water consumption for irrigation. Design/methodology/approach The IoT-based smart irrigation system designed with various sensors to collect farm field data, and stored all the data in the cloud for scheduling the irrigation. Findings This system reduces the water and electricity consumption, and labor cost. Research limitations/implications Difficult to implement on a small farm field with different crops. Practical implications Crop type, soil type and environment data should be considered for better saving of water. Social implications Reduces the water consumption, electricity, man power and increase production. Originality/value The soil type, crop type and environment data have been added before irrigation. The climate data also included before scheduling. Dynamic changing of irrigation timings based on the climate and sensor data.

The growth of digital documents on web becomes the massive sources for online market analyzing at broad level. The study of market research over online incorporating new parameter called sentiment analysis. The sentiment analysis plays a crucial role for identifying behavior of customers by means of natural language processing from customer feedback about product or services. The opinion mining have done from the user data over web related activities such as search history, blog activities, forums, comments on the social network, express the opinion about the concept/product and suggestion or recommendations. The present system is non-adaptive relation identification system works on existing, predetermined set of relations and it cannot identify the new type relation for opinion mining. The existing system are also neglected the static sentiments of users. This paper proposed ontology based adaptive sentiment analysis system for extracting new features added on the user space. In our work, the ontology and 3D space clustering framework which allows incorporation of domain knowledge for predicting sentimental analysis via opinion mining.