Now-a-days, people often express their opinions as reviews, comments, feedback in various social networking sites, business organizations. Feedbacks that are given by the end users have a great impact for the evolution of new version of product or service. For business invested in customers, analyzing each piece of feedback by hand can be overwhelming and similarly for an organization to rate an employee regarding his/her performance based on usual quantitative feedback system is a challenging task. Sentiment analysis, developed within this context can be helpful to solve such issues at early stage and provide guidance in improving their sales and productivity. Moreover, reviews written in natural language are mostly unstructured and needs huge time for processing. As the data is available in large size, it's impossible to process and analyze the information manually. In order to solve this issue, many machine Learning techniques and Deep Learning models are being proposed for automatic learning, extraction and analysis. As the technology advances businesses, organizations, social media and e-commerce sites can benefit from these in-depth insights and customer satisfaction can be analyzed. Sentiment analysis is an excellent source to perform fine-grained analysis like feature-based sentiment analysis and it can be used to identify different aspects expressed at either document or sentence level. This paper highlights the insights of extracting the most important aspects from the opinions expressed in the input text using various machine learning techniques.

E-Commerce product features and reviews are considered to be the essential factors in real-time e-commerce sites for product recommendation systems. Due to inaccuracy decision patterns, in most cases e-commerce user fails to predict the products based on the user ratings and review comments. Traditional sentiment classification models are independent of data filtering, transformation and sentiment score computing techniques which require high computing memory, time and mostly leading to false-positive rate. To overcome these issues, a novel sentiment score-based product recommendation model is proposed on the real-time product data. In this model, a new product ranking score, filtering, and hybrid decision tree classifiers are implemented. Initially, real-time amazon product review data is captured using Document Object Model (DOM) parser. The features from the review comments are extracted using lexicon Feature Dictionary (FD) and AFINN, Normalized Product Review Score (NPRS) are generated to compute the class label for product review sentiment prediction. Ranked Principal Component Analysis (RPCA) is used as a feature selection measure to overcome the problem of data sparsity. Random Tree, Hoeffding Tree, Adaboost + Random Tree, the three variants of decision tree classifiers are used for product sentiment classification. The proposed filter-based improved decision tree sentiment classification model for real-time amazon product review data recommends the product based on the user query by prediction using a new novel normalized product review sentiment score and ranked feature selection measure. The proposed product recommendation, the decision-making system maximizes sentiment classification accuracy. Experimental results are compared against the traditional decisionmaking classification models in terms of correctly classified instances, error rate, and PRC, F-measure, kappa statistics. The proposed model experimental results show high efficiency.

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Abstract-The Internet of Things pertains to connecting currently unconnected things and people. It is the new era in transforming the existed systems to amend the cost effective quality of services for the society. To support Smart city vision, Urban IoT design plans exploit added value services for citizens as well as administration of the city with the most advanced communication technologies. To make emergency response real time, IoT enhances the way first responders and provides emergency managers with the necessary up-to-date information and communication to make use of those assets. IoT mitigates many of the challenges to emergency response including present problems, like a weak communication network and information lag. In this paper, it is proposed that an emergency response system for fire hazards is designed by using IoT standardized structure. To implement this proposed scheme a low-cost Expressive wi-fi module ESP-32, Flame detection sensor, Smoke detection sensor (MQ-5), Flammable gas detection sensor and one GPS module are used. The sensors detects the hazard and alerts the local emergency rescue organizations like fire departments and police by sending the hazard location to the cloud-service through which all are connected. The overall network utilizes a light weighted data oriented publish-subscribe message protocol MQTT services for fast and reliable communication. Thus, an intelligent integrated system is designed with the help of IoT.