Sentiment Analysis of Restaurant Reviews
01 Jan 2019-pp 383-390
TL;DR: This project provides a way of sentiment analysis using data mining techniques which will process the huge amount of product review data faster and apply an algorithm to extract a meaningful result.
Abstract: The project is on sentiment analysis on a data set retrieved from a data warehouse. For example, Amazon, one of the largest online shopping sites receives orders and reviews in millions every day. This huge amount of raw data can be used for industrial or business purpose by organizing according to our requirement and processing. This project provides a way of sentiment analysis using data mining techniques which will process the huge amount of product review data faster. We are going to work on this dataset of reviews and apply an algorithm to extract a meaningful result. The following literature survey illuminates the meaning of sentiment analysis, the various current methodologies of implementation, and the future scope in this domain.
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TL;DR: Various communication protocols, namely Zigbee, Bluetooth, Near Field Communication (NFC), LoRA, etc. are presented, and the difference between different communication protocols is provided.
Abstract: Internet of Things (IoT) consists of sensors embed with physical objects that are connected to the Internet and able to establish the communication between them without human intervene applications are industry, transportation, healthcare, robotics, smart agriculture, etc. The communication technology plays a crucial role in IoT to transfer the data from one place to another place through Internet. This paper presents various communication protocols, namely Zigbee, Bluetooth, Near Field Communication (NFC), LoRA, etc. Later, it provides the difference between different communication protocols. Finally, the overall discussion about the communication protocols in IoT.
66 citations
TL;DR: The proposed work applied transfer learning classification models on both fake news and extremist-non-extremist datasets to check the performance of transfer learning models.
Abstract: Text Classification problem has been thoroughly studied in information retrieval problems and data mining tasks. It is beneficial in multiple tasks including medical diagnose health and care department, targeted marketing, entertainment industry, and group filtering processes. A recent innovation in both data mining and natural language processing gained the attention of researchers from all over the world to develop automated systems for text classification. NLP allows categorizing documents containing different texts. A huge amount of data is generated on social media sites through social media users. Three datasets have been used for experimental purposes including the COVID-19 fake news dataset, COVID-19 English tweet dataset, and extremist-non-extremist dataset which contain news blogs, posts, and tweets related to coronavirus and hate speech. Transfer learning approaches do not experiment on COVID-19 fake news and extremist-non-extremist datasets. Therefore, the proposed work applied transfer learning classification models on both these datasets to check the performance of transfer learning models. Models are trained and evaluated on the accuracy, precision, recall, and F1-score. Heat maps are also generated for every model. In the end, future directions are proposed.
29 citations
28 Nov 2020
TL;DR: In this article, the authors analyzed the customer reviews about various restaurants across Karachi - one of the biggest cities of Pakistan, by using text categorization techniques, comments are automatically classified according to feedback about food taste, ambiance, service, and value for money.
Abstract: In the last few years use of social networking sites has increased tremendously. People use social media platforms to share their views on almost all subjects. These views are in various forms like, blogs, tweets, Facebook posts, online discussion boards, Instagram posts, etc. Sentiment analysis deals with the process of computationally defining and classifying the views expressed in the comment, post or document. Typically, the aim of sentiment analysis is to find out the customer's attitude towards a product or service. Customers' feedback is vital for businesses, and social media being a powerful platform, can be used to improve and enhance business opportunities if the feedback on social media can be analyzed timely. Therefore, the focus of this paper is to analyze the customer reviews about various restaurants across Karachi - one of the biggest cities of Pakistan. For this research, customer reviews are collected from a very popular Facebook community- the SWOT'S guide to Karachi's restaurants. The contribution of this research is twofold. First, it performs sentiment analysis and classifies each comment as positive, negative. Second, by using text categorization techniques, comments are automatically classified according to feedback about food taste, ambiance, service, and value for money. A manually annotated dataset of around 4000 records was used for training and testing. Several algorithms were used for classification, including Naive Bayes Classifier, Logistic Regression, Support Vector Machine (SVM), and Random Forest. The performance comparison of these algorithms is presented. The best results, that is 95% accuracy, were achieved by using a random forest algorithm.
17 citations
05 Apr 2023
TL;DR: In this article , the authors conducted sentiment analysis on online restaurant reviews given by customers and classified each and every comment given in the text into five labels i.e. positive, negative, neutral, highly positive and highly negative.
Abstract: In recent years, the popularity of social networking sites has skyrocketed. Social media allows individuals to express their opinions on almost every topic. These opinions take variety of ways, such as tweets, Instagram posts, blogs, Facebook posts, and so on. The process of describing and categorizing the opinions represented in the form of post, document or comment is known as sentiment analysis. The goal of sentiment analysis is typically to determine the attitude of customers toward a good or service. Customer's feedback is critical for organizations and social media that can serve as a promising tool to enhance and improve business opportunities if reviews have been evaluated on social media in a timely manner. As a result, the purpose of this study is to examine online restaurant reviews given by customers. The proposed research makes two contributions. First, it conducts sentiment analysis (SA) and categorizes each and every comment given in the text into five labels i.e.; positive, negative, neutral, highly positive and highly negative. Second, comments are automatically characterized using text categorization techniques based on the feedback from the customers about taste, service, ambiance, food, and value for money. For training and testing, dataset available from Kaggle was used. For classification, numerous algorithms were employed, such as the Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes Classifier (NB). This method evaluates the performance of these algorithms and random forest algorithm produced the best results, with highest accuracy.
References
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06 Dec 2004
TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
Abstract: MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper.
Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system.
Our implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google's clusters every day.
20,309 citations
TL;DR: This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
Abstract: MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.
17,663 citations
TL;DR: The history, current use, and future of opinion mining and sentiment analysis are discussed, along with relevant techniques and tools.
Abstract: The Web holds valuable, vast, and unstructured information about public opinion. Here, the history, current use, and future of opinion mining and sentiment analysis are discussed, along with relevant techniques and tools.
1,042 citations
TL;DR: The precise method for predicting opinions enable us, to extract sentiments from the web and foretell online customer's preferences, which could prove valuable for marketing research.
Abstract: important part to gather the information is always seems as what the people think. The growing availability of opinion rich resources like online review sites and blogs arises as people can easily seek out and understand the opinions of others. Users express their views and opinions regarding products and services. These opinions are subjective information which represents user's sentiments, feelings or appraisal related to the same. The concept of opinion is very broad. In this paper we focus on the Classification of opinion mining techniques that conveys user's opinion i.e. positive or negative at various levels. The precise method for predicting opinions enable us, to extract sentiments from the web and foretell online customer's preferences, which could prove valuable for marketing research. Much of the research work had been done on the processing of opinions or sentiments recently because opinions are so important that whenever we need to make a decision we want to know others' opinions. This opinion is not only important for a user but is also useful for an organization. KeywordsMining; Machine learning; Sentiments; Polarity; semantic.
58 citations
TL;DR: A unified big data platform for social TV analytics, extracting valuable insights from TV social response in a real-time manner is envisioned and developed, providing on-demand virtual machines and reconfigurable networks.
Abstract: The TV experience is being transformed with online social networks TV audiences are sharing their opinions (ie, social response) about video programs on OSNs (eg, Twitter and Sina Weibo), thus providing a great opportunity for mining these data for stakeholders in TV value chains This new paradigm is touted as social TV analytics, integrating the emerging big data research into TV In this article, we envision and develop a unified big data platform for social TV analytics, extracting valuable insights from TV social response in a real-time manner Such a platform presents tremendous challenges in networking architecture for our big data platform We propose to build a cloud-centric platform with SDN support, providing on-demand virtual machines and reconfigurable networks The architecture of our system consists of three key components, including a robust data crawler system, an SDN-enabled big data processing system, and a social media analytics system The data crawler system adopts a distributed architecture to circumvent the access constraints of OSNs to crawl sufficient data about each TV program of interest; the SDN-enabled big data processing system integrates SDN and Hadoop, and exploits the SDN benefit to transfer intermediate data between different processing units to accelerate the data processing rate; and the social media analytics system extracts the public perception and knowledge related to TV programs based on microblog data We have built a proof-of-concept demo over a private cloud at Nanyang Technological University Feature verification and performance comparisons demonstrate the feasibility and effectiveness of the system
51 citations