A Sentiment-Based Hotel Review Summarization Using Machine Learning Techniques

Abstract

With the advent of social media, there is a data abundance so that analytics can be reliably designed for ultimately providing valuable information towards a given product or service. In this paper, we examine the problem of classifying hotel critiques using views expressed in users' reviews. There is a massive development of opinions and reviews on the web, which invariably include assessments of products and services, and beliefs about events and persons. In this study, we aim to face the problem of the forever increasing amount of opinionated data that is published in a variety of data sources. The intuition is the extraction of meaningful services despite the lack of sufficient existing architectures. Another important aspect that needs to be taken into consideration when dealing with brand monitoring, relates to the rapid heterogeneous data processing, which is vital to be implemented in real-time in order for the business to react in a more immediate way.

An Entropy-based Evaluation for Sentiment Analysis of Stock Market Prices using Twitter Data

“…A similar to the current work is the one explored in [10], where authors explore the effectiveness of social network analysis as well as sentiment analysis in predicting trends by mining publicly available online data sources. In our previous works, we have utilized cloud-based architectures aiming at creating sentiment analysis tools for Twitter data, based on Apache Spark framework [2], [4], [14].…”

“…The stock prices of both Apple and Microsoft shares have been retrieved using Quandl's platform 4 that is a powerful source for financial and economic, serving investment professionals. The estimated stock prices for the test data is compared to the actual prices derived from the dataset.…”