Multi Class Sentiment Analysis Approaches Comparison On Hotel Reviews
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Multi-class Sentiment Analysis Approaches Comparison on Hotel Reviews
Love it or hate it? 5 Star or 1 Star? Reviews are one of the most significant inputs in a customer's buying decisions. This sentiment is especially true when it comes to booking hotels. But the sheer volume of online reviews is difficult for a human to process and extract all the meaningful information. This what automatic sentiment analysis techniques come for. Different methods have been explored for sentiment analysis in previous research. the most popular three categories (see Figure 1) are lexicon-based approach, learning-based approach and hybrid approach. In my paper, I chose five supervised learning-based models Naive Bayes, Support Vector Machine, Logistic Regression, Random Forest and Convolutional Neural Network for experiment comparison. These five models are divided into two groups based on the methods to extract features for prediction or classification. Naive Bayes, Support Vector Machine, Logistic Regression, Random Forest use Bag of Words to extract features while Convolutional Neural Networks, CNN for short, use word embedding modeling to extract features. Neural networks have been really popular and have been proved to be powerful models in many fields. Is it a perfect model for everything? Will it beat traditional classifiers by score easily? That's what my experiments try to find out. There are quite a few evaluation metrics out there. I decided to use accuracy and run time as the main metrics for Word Embedding model. As for Bag of Words models, I chose to use accuracy, recall, precision, F-score and runtime. The paper is organized as this: Section 1 as always is an introduction part. section 2 covers the related work in sentiment analysis (including common techniques, new trends.) Section 3 gives more details for the models I used in this paper. Section 4 gives some background information for the metrics. Section 5 describes the details on how to implement the algorithms in depth. In section 6, results and discussion are presented with multiple charts and figures. Conclusion is in section 7 and section 8 discusses the limitations and future work.
Second International Conference on Image Processing and Capsule Networks
Author: Joy Iong-Zong Chen
language: en
Publisher: Springer Nature
Release Date: 2021-09-09
This book includes the papers presented in 2nd International Conference on Image Processing and Capsule Networks [ICIPCN 2021]. In this digital era, image processing plays a significant role in wide range of real-time applications like sensing, automation, health care, industries etc. Today, with many technological advances, many state-of-the-art techniques are integrated with image processing domain to enhance its adaptiveness, reliability, accuracy and efficiency. With the advent of intelligent technologies like machine learning especially deep learning, the imaging system can make decisions more and more accurately. Moreover, the application of deep learning will also help to identify the hidden information in volumetric images. Nevertheless, capsule network, a type of deep neural network, is revolutionizing the image processing domain; it is still in a research and development phase. In this perspective, this book includes the state-of-the-art research works that integrate intelligent techniques with image processing models, and also, it reports the recent advancements in image processing techniques. Also, this book includes the novel tools and techniques for deploying real-time image processing applications. The chapters will briefly discuss about the intelligent image processing technologies, which leverage an authoritative and detailed representation by delivering an enhanced image and video recognition and adaptive processing mechanisms, which may clearly define the image and the family of image processing techniques and applications that are closely related to the humanistic way of thinking.
Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines
Author: Management Association, Information Resources
language: en
Publisher: IGI Global
Release Date: 2022-06-10
The rise of internet and social media usage in the past couple of decades has presented a very useful tool for many different industries and fields to utilize. With much of the world’s population writing their opinions on various products and services in public online forums, industries can collect this data through various computational tools and methods. These tools and methods, however, are still being perfected in both collection and implementation. Sentiment analysis can be used for many different industries and for many different purposes, which could better business performance and even society. The Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines discusses the tools, methodologies, applications, and implementation of sentiment analysis across various disciplines and industries such as the pharmaceutical industry, government, and the tourism industry. It further presents emerging technologies and developments within the field of sentiment analysis and opinion mining. Covering topics such as electronic word of mouth (eWOM), public security, and user similarity, this major reference work is a comprehensive resource for computer scientists, IT professionals, AI scientists, business leaders and managers, marketers, advertising agencies, public administrators, government officials, university administrators, libraries, students and faculty of higher education, researchers, and academicians.