A model for predicting price polarity of real estate properties using information of real estate market websites

This paper presents a model that uses the information that sellers publish in real estate market websites to predict whether a property has higher or lower price than the average price of its similar properties. The model learns the correlation between price and information (text descriptions and features) of real estate properties through automatic identification of latent semantic content given by a machine learning model based on doc2vec and xgboost. The proposed model was evaluated with a data set of 57,516 publications of real estate properties collected from 2016 to 2018 of Bogot\'a city. Results show that the accuracy of a classifier that involves text descriptions is slightly higher than a classifier that only uses features of the real estate properties, as text descriptions tends to contain detailed information about the property

Survey on product review sentiment classification and analysis challenges

There is no doubt that the process of using the internet to post comments and to get others' comments has become a common daily practice on the Web. Nowadays, a huge amount of information is available on the internet. The data which is posted by users and customers who visit these websites every day contain significant information. Some companies ask their customers about a product or services, for feedback analysis and to evaluate the satisfaction ratio of their products and services. The reviews by customers of products are rapidly growing. This paper provides ground knowledge and covers the most important scholarly papers and research that have been done in the area of sentiment analysis and the classification of opinion. This work presents opinion definitions and more detailed opinion classifications, and explains the related topics. This review will provide an introduction to the most common and significant information related to sentiment analysis, and it will answer many questions that have been asked in opinion mining, analysis, classifications and challenges