HappyMeter: An Automated System for Real-Time Twitter Sentiment Analysis
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 8
Abstract
The paper presents HappyMeter, an automated system for real-time Twitter sentiment analysis. More than 380 million tweets consisting of nearly 30,000 words, almost 6,000 hashtags and over 5,000 user mentioned have been studied. A sentiment model is used to measure the sentiment level of each term in the contiguous United States. The system automatically mines real-time Twitter data and reveals the changing patterns of the public sentiment over an extended period of time. It is possible to compare the public opinions regarding a subject, hashtag or a Twitter user between different states in the U.S. Users may choose to see the overall sentiment level of a term, as well as its sentiment value on a specific day. Real-time results are delivered continuously and visualized through a web-based graphical user interface.
Authors and Affiliations
Joaquim Perotti Canela, Tina Tian
Keywords
Related Articles
- EP ID EP259963
- DOI 10.14569/IJACSA.2017.080801
- Views 58
- Downloads 0
Impact of Android Phone Rooting on User Data Integrity in Mobile Forensics
Modern cellular phones are potent computing de-vices, and their capabilities are constantly progressing. The Android operating system (OS) is widely used, and the number of accessible apps for Android OS phones is unprec...
Learning Deep Transferability for Several Agricultural Classification Problems
This paper addresses several critical agricultural classification problems, e.g. grain discoloration and medicinal plants identification and classification, in Vietnam via combining the idea of knowledge transferability...
Case Based Reasoning: Case Representation Methodologies
Case Based Reasoning (CBR) is an important technique in artificial intelligence, which has been applied to various kinds of problems in a wide range of domains. Selecting case representation formalism is critical for the...
Image Retrieval System based on Color Global and Local Features Combined with GLCM for Texture Features
In CBIR (content-based image retrieval) features are extracted based on color, texture, and shape. There are many factors affecting the accuracy (precision) of retrieval such as number of features, type of features (loca...
Multi-Agent Based Model for Web Service Composition
The evolution of the Internet and the competitiveness among companies were factors in the explosion of Web services. Web services are applications available on the Internet each performing a particular task. Web users of...