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.

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>

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.

A rule based approach for aspect extraction in hindi reviews

2021 ◽  
pp. 1-9
Author(s):  
Chinmayee Ojha ◽  
Manju Venugopalan ◽  
Deepa Gupta
Keyword(s):  
Public Opinion ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Fast Growth ◽  
Rule Based ◽  
The Public ◽  
Aspect Extraction ◽  
Automated Process ◽  
Rule Based Approach ◽  
Baseline Approach

Fast growth of technology and the tremendous growth of population has made millions of people to be active participants on social networking forums. The experiences shared by the participants on different websites is highly useful not only to customers to make decisions but also helps companies to maintain sustainability in businesses. Sentiment analysis is an automated process to analyze the public opinion behind certain topics. Identifying targets of user’s opinion from text is referred to as aspect extraction task, which is the most crucial and important part of Sentiment Analysis. The proposed system is a rule-based approach to extract aspect terms from reviews. A sequence of patterns is created based on the dependency relations between target and its nearby words. The system of rules works on a benchmark of dataset for Hindi shared by Akhtar et al., 2016. The evaluated results show that the proposed approach has significant improvement in extracting aspects over the baseline approach reported on the same dataset.

Political Tweets Anatomization Using Sentiment Analysis

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Atul Pawar ◽  
Sairaj Lohar ◽  
Manoj Patil ◽  
Rushikesh Kulkarni ◽  
Vasim Inamdar
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Learning Algorithm ◽  
Modern Society ◽  
Electoral Politics ◽  
Political Campaigns ◽  
The Public ◽  
The World ◽  
The Web

Social Media has an impact on different aspects of our life. It has revolutionized the way people communicate and socialize on the web. It is an undeniable force in modern society. It has the power to force necessary changes. Social networking platforms such as Facebook, Twitter, Instagram allows users to interact with the world, to express their view on different topics in society. Social media influence in political campaigns has increased tremendously, it plays an important role in electoral politics, political polling, so it is necessary to perform sentiment analysis on political topics to get the public opinion. This technical paper focuses mainly on analysis of political tweets using sentiment analysis. In this paper, we performed sentiment analysis on different political topics in India and analysed the overall sentiments regarding those topics, using naive bayes machine learning algorithm and classified those tweets as positive and negative.

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 A Crowdsourcing Based Framework for Sentiment Analysis

2020 ◽  
Vol 16 (4) ◽  
pp. 285-295
Author(s):  
Fatima Zohra Ennaji ◽  
Abdelaziz El Fazziki ◽  
Hasna El Alaoui El Abdallaoui ◽  
Hamada El Kabtane
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Real Life ◽  
Data Sources ◽  
Valuable Data ◽  
Analysis Process ◽  
Validity Check ◽  
Online Platforms ◽  
Majority Consensus

As social networking has spread, people started sharing their personal opinions and thoughts widely via these online platforms. The resulting vast valuable data represent a rich source for companies to deduct their products’ reputation from both social media and crowds’ judgments. To exploit this wealth of data, a framework was proposed to collect opinions and rating scores respectively from social media and crowdsourcing platform to perform sentiment analysis, provide insights about a product and give consumers’ tendencies. During the analysis process, a consumer category (strict) is excluded from the process of reaching a majority consensus. To overcome this, a fuzzy clustering is used to compute consumers’ credibility. The key novelty of our approach is the new layer of validity check using a crowdsourcing component that ensures that the results obtained from social media are supported by opinions extracted directly from real-life consumers. Finally, experiments are carried out to validate this model (Twitter and Facebook were used as data sources). The obtained results show that this approach is more efficient and accurate than existing solutions thanks to our two-layer validity check design.

scholarly journals Stock Market Increase and Decrease using Twitter Sentiment Analysis and ARIMA Model

2021 ◽  
Vol 12 (1S) ◽  
pp. 146-161
Author(s):  
S. V. Kedar
Keyword(s):  
Social Networks ◽  
Time Series ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Stock Exchange ◽  
Arima Model ◽  
Forecasting Model ◽  
Company Performance ◽  
The People ◽  
A Company

With centuries and decades, people started evolving and slowly started entering into technology era. Social networks era came before everyone which connected people from far away countries. Such an example of social network are applications like Twitter, Facebook, Instagram, LinkedIn etc. Every application has its own significance. Such an application is Twitter where people tweet regarding their opinion about a topic, a person anything. The tweets regarding company its performance and people’s opinion about the stock is also tweeted. People like to invest in stocks using this data posted of social networking. This data keeps them updated about a company. In this paper, we will be using tweets related to stocks so that we can analyse sentiments of people regarding a particular stock. This sentiment analysis can provide a feedback about the company so that we will be able to understand an increase or decrease with respect to the people or company performance. In later stages, we will be comparing this analysis with ARIMA model which is time series forecasting model. ARIMA takes values of stocks and predicts its future prices based on its algorithm. Using both of these techniques a cumulative result for stock exchange will be obtained. The dataset is the fresh tweets taken from twitter and also the stock data will be imported directly for ARIMA.

scholarly journals WHO CONTROLS THE ARAB PUBLIC SPHERE IN THE ERA OF NEW MEDIA, THE NEW REVOLUTIONISTS OR THE DEEP STATE?

2020 ◽  
Vol 12 ◽  
pp. 312-323
Author(s):  
Nawaf Abdelhay Altamimi
Keyword(s):  
New Media ◽  
Public Sphere ◽  
Social Networking ◽  
Mobile Phones ◽  
Social Networking Sites ◽  
Arab Countries ◽  
Web Based ◽  
The Public ◽  
Deep State ◽  
The Web

Recent events in Arab countries, particularly in Tunisia, Egypt have shown that new modes of communications such as Mobile phones and social networking sites have facilitated civil society's organization by allowing a timely exchange of opinions and ideas. Youth protesters in uprising societies have recognised the value of Mechanisms in which the public can meet and discuss and share ideas openly, recognise problems and suggest solutions (Caplan and Boyd, 2016). Those Young demonstrators have taken to social media such as Facebook and Twitter online to organize social prodemocracy movements and start the revolution, demonstrating how the Web-based platforms have become a crucial alternative media instrument for advocacy in today's Digital Age. (Kenix, 2009).

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