Online Reviews System using Aspect Based Sentimental Analysis & Opinion Mining

Aspect extraction is the most critical and thoroughly researched process in SA (Sentiment Analysis) for conducting an accurate classification of feelings. Over the last decade, massive amounts of research have focused on identifying and removing elements. Products have centralized distribution channels, and certain apps may occasionally operate close to the most recent product to be created. Any e-commerce business enterprise must analyses user / customer feedback in order to provide better products and services to them. Because broad reviews frequently include remarks in a consolidated manner when a customer gives his thoughts on various product attributes within the same summary, it is difficult to determine the exact feeling. The key components of this software are included in their release, making it a valuable tool for management to improve the consistency of their own system's specifications. The goal was to categories the aspects of the target entities provided, as well as the feelings conveyed for each aspect. First, we are implementing a supervised classification framework that is tightly restricted and relies solely on training sets for knowledge. As a result, the key terms comes from associated at various elements of a thing within its entirety perform customer sentiment using certain elements. In contrast to current sentiment analysis approaches, synthetic and actual data set experiments yield positive results

Local Industrialization Based Lucrative Farming Using Machine Learning Technique

In recent times, agriculture have gained lot of attention of researchers. More precisely, crop prediction is trending topic for research as it leads agri-business to success or failure. Crop prediction totally rest on climatic and chemical changes. In the past which crop to promote was elected by rancher. All the decisions related to its cultivation, fertilizing, harvesting and farm maintenance was taken by rancher himself with his experience. But as we can see because of constant fluctuations in atmospheric conditions coming to any conclusion have become very tough. Picking correct crop to grow at right times under right circumstances can help rancher to make more business. To achieve what we cannot do manually we have started building machine learning models for it nowadays. To predict the crop deciding which parameters to consider and whose impact will be more on final decision is also equally important. For this we use feature selection models. This will alter the underdone data into more precise one. Though there have been various techniques to resolve this problem better performance is still desirable. In this research we have provided more precise & optimum solution for crop prediction keeping Satara, Sangli, Kolhapur region of Maharashtra. Along with crop & composts to increase harvest we are offering industrialization around so rancher can trade the yield & earn more profit. The proposed solution is using machine learning algorithms like KNN, Random Forest, Naïve Bayes where Random Forest outperforms others so we are using it to build our final framework to predict crop