scholarly journals MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 115 ◽  
Author(s):  
Yaocheng Zhang ◽  
Wei Ren ◽  
Tianqing Zhu ◽  
Ehoche Faith
Keyword(s):  
Sentiment Analysis ◽  
Interaction Design ◽  
Mobile Application ◽  
Situational Awareness ◽  
Mobile Internet ◽  
The Public ◽  
Public Sentiment ◽  
High Altitude Area ◽  
Sentiment Dictionary ◽  
Analysis System

The development of mobile internet has led to a massive amount of data being generated from mobile devices daily, which has become a source for analyzing human behavior and trends in public sentiment. In this paper, we build a system called MoSa (Mobile Sentiment analysis) to analyze this data. In this system, sentiment analysis is used to analyze news comments on the THAAD (Terminal High Altitude Area Defense) event from Toutiao by employing algorithms to calculate the sentiment value of the comment. This paper is based on HowNet; after the comparison of different sentiment dictionaries, we discover that the method proposed in this paper, which use a mixed sentiment dictionary, has a higher accuracy rate in its analysis of comment sentiment tendency. We then statistically analyze the relevant attributes of the comments and their sentiment values and discover that the standard deviation of the comments’ sentiment value can quickly reflect sentiment changes among the public. Besides that, we also derive some special models from the data that can reflect some specific characteristics. We find that the intrinsic characteristics of situational awareness have implicit symmetry. By using our system, people can obtain some practical results to guide interaction design in applications including mobile Internet, social networks, and blockchain based crowdsourcing.

scholarly journals Opinion Mining and Sentiment Analysis on Big Data

2020 ◽  
pp. 410-415
Author(s):  
Shruti Rajkumar Choudhary
Keyword(s):  
Artificial Intelligence ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Opinion Mining ◽  
Stock Exchange ◽  
Daily Basis ◽  
Text Data ◽  
The Public ◽  
Public Sentiment

<p>Opinion mining is extract subjective information from text data using tools such as NLP, text analysis etc. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product.In this project the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in terms of positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.</p>

Analysis of Public Sentiments About Mega Online Sale Using Tweets on Big Billions Day Sale

2019 ◽  
pp. 59-76 ◽  
Author(s):  
Dilip Singh Sisodia ◽  
Ritvika Reddy
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Stock Exchange ◽  
The Public ◽  
Online Sales ◽  
Public Sentiment ◽  
Online Platforms ◽  
Wide Range ◽  
Automated Process ◽  
The Web

The opinion of others significantly influences our decision-making process about any product or service. The positive or negative opinions of prospective clients or customers may promote or demote the profit margin of any business activities. Therefore, analyzing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections, and predicting socioeconomic phenomena such as stock exchange, sale of products, etc. With the emergence of Web 2.0 services, a wide range of online platforms including micro-blogging, social networking, and many other review platforms are available. The automated process for public sentiment analysis from a large amount of social data present on the web helps to improve customer satisfaction. This chapter discusses the process of sentiment analysis of prospective buyers of mega online sales using their posted tweets about the big billions day sale.

PUBLIC SENTIMENT ANALYSIS OF PASAR LAMA TANGERANG USING K-NEAREST NEIGHBOR METHOD AND PROGRAMMING LANGUAGE R

2019 ◽  
Vol 24 (2) ◽  
pp. 129-133
Author(s):  
Hustinawaty ◽  
Rama Al Azis Dwiputra ◽  
Tavipia Rumambi
Keyword(s):  
Sentiment Analysis ◽  
Programming Languages ◽  
Nearest Neighbor ◽  
Tourist Attraction ◽  
K Nearest Neighbor ◽  
The Public ◽  
Public Sentiment ◽  
A Value ◽  
Negative Comments ◽  
The City

Pasar Lama Tangerang is a tourist attraction in the city of Tangerang. With the development of current technology, the public can provide an overview of how the facilities and services are provided by expressing opinions on the internet. However, it is difficult to distinguish which opinions belong to positive or negative opinions. Sentiment analysis is needed to overcome this problem. The stage in sentiment analysis starts with collecting data first, then the data is processed. Furthermore, the data that has been propagated is given a sentiment classification using the K-Nearest Neighbor (KNN) algorithm. Then the classification results obtained an accuracy of 83% with a value of k = 1 of 120 data divided by 92 positive and 28 negative comments. Sentiment analysis is made using the R and Rstudio programming languages as supporting software.

scholarly journals Opinion Mining Analysis of Twitter Users of Particular Topic

2020 ◽  
pp. 102-108
Author(s):  
Sneha Naik ◽  
Mona Mulchandani
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Stock Exchange ◽  
Daily Basis ◽  
The Public ◽  
Public Sentiment ◽  
Twitter Users

Opinion mining consists of many different fields like natural language processing, text mining, decision making and linguistics. Opinion mining is a type of natural language processing for tracking the mood of the public about a particular product. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. This project addresses the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.

scholarly journals Visualization of Real-time Twitter Data based on Sentiment Classification

2020 ◽  
Vol 9 (4) ◽  
pp. 1868-1872
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Real Time ◽  
Social Networking Sites ◽  
Political Issues ◽  
The Public ◽  
Public Sentiment ◽  
Challenges And Opportunities ◽  
Product Personality ◽  
Bayes Algorithm

Analyzing information from social media sites could bring great challenges and opportunities to solve many real time problems. It gives the public opinion about almost every product, personality or any service. The data from social networking sites is more accurate and useful to analyze the public sentiment about the trending topics. The activity of analyzing opinions, sentiments and also the subjectivity of data that is provided, is called sentiment analysis. Tweepy is an easy-to-use python library which is used to extract source data from twitter. From these tweets, features are extracted and then classified using Naïve Bayes algorithm to identify sentiment. This aims to provide an interactive automatic system which predicts the sentiment of the tweets posted in social media using python in real-time. These applications of sentiment analysis are broad and they tend to be very useful in today’s lifestyle. It will evaluate people's sentiment about the trends, entertainment, political issues and products which helps to improve marketing strategies with the help of hashtags, keywords etc.

scholarly journals Movie Recommendation System Mistreatment Current Trends and Sentiment Analysis from Micro Blogging Knowledge

2021 ◽  
Vol 9 (11) ◽  
pp. 393-398
Author(s):  
J Manikandan
Keyword(s):  
Collaborative Filtering ◽  
Sentiment Analysis ◽  
Digital Media ◽  
Recommendation System ◽  
The Public ◽  
Public Sentiment ◽  
Current Trends ◽  
Traditional Approaches ◽  
User History ◽  
Content Based Filtering

Abstract: Recommendation systems (RSs) have garnered immense interest for applications in e-commerce and digital media. Traditional approaches in RSs include such as collaborative filtering (CF) and content-based filtering (CBF) through these approaches that have certain limitations, such as the necessity of prior user history and habits for performing the task of recommendation. To minimize the effect of such limitation, this article proposes a hybrid RS for the movies that leverage the best of concepts used from CF and CBF along with sentiment analysis of tweets from microblogging sites. The purpose to use movie tweets is to understand the current trends, public sentiment, and user response of the movie. Experiments conducted on the public database have yielded promising results. Keywords: Collaborative filtering, Content based filtering, Recommendation System, Sentiment Analysis, Twitter

scholarly journals Implementasi Metode Klasifikasi Naive Bayes Pada Sistem Analisis Opini Pengguna Twitter Berbasis Web

2021 ◽  
Vol 10 (1) ◽  
pp. 46-54
Author(s):  
Apif Supriadi ◽  
Fatmasari
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Application Programming Interface ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Public Sentiment ◽  
Application Programming ◽  
Analysis System ◽  
Programming Interface

Abstract— Development of social media which is the result of technological development is an inseparable part of people's lives. Social media is a place where ordinary people express their feelings and opinions about something that concerns them. Inknowing the direction of public sentiment, surveys are usually done online or offline, this sentiment analysis system will facilitate and speed up the process of knowing the direction of public sentiment, in the case of research. This uses data from Twitter social media called tweets or tweets, web-based sentiment analysis system that will classify tweets into 3 (three) types of sentiments, namely positive, neutral and negative, then make a percentage to make it easier to see the direction of public sentiment. In classifying this system uses the Naive Bayes Classifier method and displays it in a web interface with the PHP programming language and uses the Application Programming Interface (API) to get data from Twitter. Intisari — Saat ini perkembangan media sosial yang merupakan hasil dari perkembangan teknologi menjadi bagian tak terpisahkan dari kehidupan masyarakat. Media sosial menjadi tempat masyarakat biasa mengutarakan berbagai perasaan dan opininya tentang suatu hal yang jadi perhatian mereka, dalam mengetahui arah sentimen masyarakat biasanya dilakukan survei baik secara online atau offline, sistem analisis sentimen ini akan memudahkan dan mempercepat proses mengetahui arah sentimen publik, dalam kasus penelitian ini menggunakan data dari media sosial Twitter yang disebut dengan tweets atau cuitan, sistem analisis sentimen berbasis web yang akan mengklasifikasikan cuitan kedalam 3 (tiga) jenis sentimen yaitu positif, netral dan negatif lalu melakukan persentasenya agar mempermudah melihat arah sentimen publik. Dalam melakukan klasifikasinya sistem ini menggunakan metode Naive Bayes Classifier dan menampilkannya dalam antarmuka web dengan bahasa pemrograman PHP dan menggunakan Application Programming Interface (API) dalam mendapatkan data dari Twitter.

A cross-media public sentiment analysis system for microblog

2014 ◽  
Vol 22 (4) ◽  
pp. 479-486 ◽  
Author(s):  
Donglin Cao ◽  
Rongrong Ji ◽  
Dazhen Lin ◽  
Shaozi Li
Keyword(s):  
Sentiment Analysis ◽  
Cross Media ◽  
Public Sentiment ◽  
Analysis System
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