scholarly journals Sentiment Trend Analysis of Big Data

2019 ◽  
Vol 8 (10) ◽  
pp. 59-64
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Sentiment Analysis ◽  
Real Time ◽  
Language Processing ◽  
Opinion Mining ◽  
Data Sources ◽  
Product Reviews

Various fields like Text Mining, Linguistics, Decision Making and Natural Language Processing together form the basis for Opinion Mining or Sentiment Analysis. People share their feelings, observations and thoughts on social media, which has emerged as a powerful tool for rapidly growing enormous repository of real time discussions and thoughts shared by people. In this paper, we aim to decipher the current popular opinions or emotions from various sources, hence, contributing to sentiment analysis domain. Text from social media, blogs and product reviews are classified according to the sentiment they project. We re-examine the traditional processes of sentiment extraction, to incorporate the increase in complexity and number of the data sources and relevant topics, while re-populating the meaning of sentiment. Working across and within numerous streams of social media, expression of sentiment and classification of polarity is re-examined, thereby redefining and enhancing the realm of sentiment. Numerous social media streams are analyzed to build datasets that are topical for each stream and are later polarized according to their sentiment expression. In conclusion, defining a sentiment and developing tools for its analysis in real time of human idea exchange is the motive.

scholarly journals Application of Naïve Bayes Algorithm in Sentiment Analysis of Filipino, English and Taglish Facebook Comments

2020 ◽  
Vol 4 (5) ◽  
pp. 73-77
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Product Reviews ◽  
Documentary Data ◽  
The Social ◽  
Bayes Algorithm

The World Wide Web has boosted its content for the past years, it has a vast amount of multimedia resources that continuously grow specifically in documentary data. One of the major contributors of documentary contents can be evidently found on the social media called Facebook. People or netizens on Facebook are actively sharing their opinion about a certain topic or posts that can be related to them or not. With the huge amount of accessible documentary data that are seen on the so-called social media, there are research trends that can be made by the researchers in the field of opinion mining. A netizen’s comment on a particular post can either be a negative or a positive one. This study will discuss the opinion or comment of a netizen whether it is positive or negative or how she/he feels about a specific topic posted on Facebook; this is can be measured by the use of Sentiment Analysis. The combination of the Natural Language Processing and the analytics in textual form is also known as Sentiment Analysis that is use to the extraction of data in a useful manner. This study will be based on the product reviews of Filipinos in Filipino, English and Taglish (mixed Filipino and English) languages. To categorize a comment effectively, the Naïve Bayes Algorithm was implemented to the developed web system.

Tasks, Approaches, and Avenues of Opinion Mining, Sentiment Analysis, and Emotion Analysis

2021 ◽  
pp. 171-209
Author(s):  
Amira M. Idrees ◽  
Fatma Gamal Eldin ◽  
Amr Mansour Mohsen ◽  
Hesham Ahmed Hassan
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Potential Benefit ◽  
Opinion Mining ◽  
Product Reviews ◽  
Huge Amount ◽  
Know How

Every successful business aims to know how customers feel about its brands, services, and products. People freely express their views, ideas, sentiments, and opinions on social media for their day-to-day activities, for product reviews, for surveys, and even for their public opinions. This process provides a fortune of valuable resources about the market for any type of business. Unfortunately, it's impossible to manually analyze this massive quantity of information. Sentiment analysis (SA) and opinion mining (OM), as new fields of natural language processing, have the potential benefit of analyzing such a huge amount of data. SA or OM is the computational treatment of opinions, sentiments, and subjectivity of text. This chapter introduces the reader to a survey of different text SA and OM proposed techniques and approaches. The authors discuss in detail various approaches to perform a computational treatment for sentiments and opinions with their strengths and drawbacks.

Polarity Classification of Arabic Sentiments

2016 ◽  
Vol 11 (3) ◽  
pp. 32-49 ◽  
Author(s):  
Mohammed N. Al-Kabi ◽  
Heider A. Wahsheh ◽  
Izzat M. Alsmadi
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Operating Characteristic ◽  
Opinion Mining ◽  
Online Social Network ◽  
The Social ◽  
Polarity Classification ◽  
Arabic Sentiment Analysis ◽  
Modern Standard

Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

scholarly journals Data Mining and Machine Learning: Design a Generalized Real Time Sentiment Analysis System on Tweeter Data Using Natural Language Processing

2019 ◽  
Vol 8 (6) ◽  
pp. 2139-2142
Keyword(s):  
Sentiment Analysis ◽  
Real Time ◽  
Language Processing ◽  
Automated System ◽  
Product Reviews ◽  
Analysis System ◽  
Result Analysis ◽  
Python Programming ◽  
The Impact ◽  
Analyze Data

Sentiment analysis is a task, that is becoming recently important for numerous companies. Because the consigner subscriptions on social media like Facebook, twitter and other side get their product reviews. If the company wants to track tweets about their brand to command over the impact on time or many website analyze the comments on their articles. This will help them to track comments and impact. So the sentiment analysis is an automated system that collects and analyzes the content and generates the desired results. This paper proposes a sentiment analysis system for twitter posts. Proposed system will work on real time tweets. System is also designed in such a way that this can analyze data related to any topic. Python programming language is used to extract tweets form twitter feeds. Proposed system also calculates the level of sentiments. That how much negative or positive tweets are. This paper also presents some real time result analysis.

scholarly journals Sentiment Analysis on Social Media Big Data With Multiple Tweet Words

2019 ◽  
Vol 8 (10) ◽  
pp. 3429-3434 ◽  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Big Data ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Sentiment Classification ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Machine Learning Classification ◽  
Tree Classifier

The main objective of this paper is Analyze the reviews of Social Media Big Data of E-Commerce product’s. And provides helpful result to online shopping customers about the product quality and also provides helpful decision making idea to the business about the customer’s mostly liking and buying products. This covers all features or opinion words, like capitalized words, sequence of repeated letters, emoji, slang words, exclamatory words, intensifiers, modifiers, conjunction words and negation words etc available in tweets. The existing work has considered only two or three features to perform Sentiment Analysis with the machine learning technique Natural Language Processing (NLP). In this proposed work familiar Machine Learning classification models namely Multinomial Naïve Bayes, Support Vector Machine, Decision Tree Classifier, and, Random Forest Classifier are used for sentiment classification. The sentiment classification is used as a decision support system for the customers and also for the business.

Modelling Propagation of Public Opinions on Microblogging Big Data Using Sentiment Analysis and Compartmental Models

2020 ◽  
pp. 939-956
Author(s):  
Youjia Fang ◽  
Xin Chen ◽  
Zheng Song ◽  
Tianzi Wang ◽  
Yang Cao
Keyword(s):  
Social Media ◽  
Big Data ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Information Diffusion ◽  
Model Fitting ◽  
Compartmental Models ◽  
Public Opinions ◽  
Estimation And Prediction

Compartmental models have been used to model information diffusion on social media. However, there have been few studies on modelling positive and negative public opinions using compartmental models. This study aimed for using sentiment analysis and compartmental model to model the propagation of positive and negative opinions on microblogging big media. The authors studied the news propagation of seven popular social topics on China's Sina Weibo microblogging platform. Natural language processing and sentiment analysis were used to identify public opinions from microblogging big data. Then two existing (SIZ and SEIZ) models and a newly developed (SE2IZ) model were implemented to model the news propagation and evaluate the trends of public opinions on selected social topics. Simulation study was used to check model fitting performance. The results show that the new SE2IZ model has a better model fitting performance than existing models. This study sheds some new light on using social media for public opinion estimation and prediction.

scholarly journals Sentiment Analysis of Review Data of a Product Using Python

2018 ◽  
Vol 7 (3.12) ◽  
pp. 674
Author(s):  
P Santhi Priya ◽  
T Venkateswara Rao
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
The Other ◽  
Product Reviews ◽  
Prime Problem

The other name of sentiment analysis is the opinion mining. It’s one of the primary objectives in a Natural Language Processing(NLP). Opinion mining is having a lot of audience lately. In our research we have taken up a prime problem of opinion mining which is theSentiment Polarity Categorization(SPC) that is very influential. We proposed a methodology for the SPC with explanations to the minute level. Apart from theories computations are made on both review standard and sentence standard categorization with benefitting outcomes. Also, the data that is represented here is from the product reviews given on the shopping site called Amazon.  

scholarly journals Online Realtime Sentiment Analysis Tweets by Utilizing Streaming API Features From Twitter

2019 ◽  
Vol 9 (1) ◽  
pp. 53
Author(s):  
Nfn Bahrawi
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Relevant Information ◽  
Processing Technique ◽  
Web Based ◽  
Analysis Process ◽  
Analysis System

<p class="JGI-AbstractIsi">Twitter is one of the social media that has a simple and fast concept, because short messages, news or information on Twitter can be more easily digested. This social media is also widely used as an object for researchers or industry to conduct sentiment analysis in the fields of social, economic, political or other fields. Opinion mining or also commonly called sentiment analysis is the process of analyzing text to get certain information in a sentence in the form of opinion. Sentiment analysis is one of the branches of the science of Text mining where text mining is a natural language processing technique and analytical method that is applied to text data to obtain relevant information. Public opinion or sentiment in social media twitter is very dynamic and fast changing, a real time sentiment analysis system is needed and it is automatically updated continuously so that changes can always be monitored, anytime and anywhere. This research builds a system so that it can analyze sentiment from twitter social media in realtime and automatically continuously. The results of the system trial succeeded in drawing data, conducting sentiment analysis and displaying it in graphical and web-based realtime and updated automatically. Furthermore, this research will be developed with a focus on the accuracy of the algorithms used in conducting the sentiment analysis process.</p>

scholarly journals Twitter Sentiment Analysis on Government Law Using Real Time Data

2021 ◽  
pp. 200-205
Author(s):  
Sujata Patil ◽  
Bhavesh Wagh ◽  
Aditya Bhinge ◽  
Aakash Sahal ◽  
Prof. Madhav Ingale
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Central Government ◽  
Research Work ◽  
Research Area ◽  
User Generated Content ◽  
Time Data ◽  
Social Data

Social media monitoring has been growing day by day so analyzing social data plays an important role in knowing people's behavior. So we are analyzing Social data such as Twitter Tweets using sentiment analysis which checks the opinion of people related to government schemes that are announced by the Central Government. This paper-based is on social media Twitter datasets of particular schemes and their polarity of sentiments. The popularity of the Internet has been rapidly increased. Sentiment analysis and opinion mining is the field of study that analyses people's opinions, sentiments, evaluations, attitudes, and emotions from written language. User-generated content is highly generated by users. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. It is difficult to analyze or summarize user-generated content. Most of the users write their opinions, thoughts on blogs, social media sites, E-commerce sites, etc. So these contents are very important for individuals, industry, government, and research work to make decisions. This Sentiment analysis and opinion mining research is a hot research area that comes under Natural Language processing. We plot and calculate numbers of positive, negative, and neutral tweets from each event.

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