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Agriculture is the very important sector of each country, where the gross domestic pay relies on it. The outcome of the agriculture or crop management was completely based on the end yield and the market rate. The complete factor of the crop yield depends on timely monitoring and suggestion. Artificial intelligence gives a way to monitor the crop and to predict the yield in an automatized outcome. The study has been made on the deep learning and its hybrid techniques such as Artificial neural network, deep neural network and Recurrent neural network. It helped to identify how the technology of artificial intelligence helps to improve the crop yield. The research study clearly gives the idea and need of recurrent neural network and hybrid network in the field of agriculture. It also shows how it outperforms the other networks such as artificial neural network and convolutional neural network. The results were analyzed and the future perspectives were drawn with the obtained outcome.

Online reviews evolve rapidly over time, which demands much more efficient and flexible algorithms for sentiment analysis than the current approaches. Current approaches detect the overall sentiment of a document, without performing an in-depth analysis to discover. We propose a Document level sentiment classification in conjunction with topic detection and topic sentiment analysis of bigrams simultaneously. This model is based on the weakly supervised Joint Sentiment-Topic model, and this extends the Latent Dirichlet Allocation by adding the sentiment layer. We considered Bigrams in ordered to increase the accuracy of sentiment analysis. We created a sentiment thesaurus with positive and negative lexicons and this is used to find the sentiment polarity of the bigrams. This model can be shifted to other domains. This is verified experimentally through four different domains which even outperforms the existing semi-supervised approaches.