A cloud-based big data sentiment analysis application for enterprises' brand monitoring in social media streams

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.

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Sentiment Analysis of Events on Social Web

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f3946.049620 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1232-1238

Keyword(s):

Social Media ◽

Big Data ◽

Sentiment Analysis ◽

Recommendation System ◽

Learning Algorithm ◽

Extraction Process ◽

Online Data ◽

Promising Area ◽

Data Volume ◽

Application Data

These days, Data volume has experienced enormous increase in volume, giving new challenges in technology and application. Data production has been expected at the rate of 2.5 Exabyte (1Ex-abyte=1.000.000Terabytes) of data per day. The main sources of data are: sensors collect climate information, traffic and flight information, social media sites (Twitter and Facebook are popular examples), digital pictures and videos (YouTube users upload 72 hours of new video content per minute), etc. Out of them social media becomes the prominent representative for the data source of big data. Social big data comes from the combination of social media and big data. Here, the data is mostly unstructured or semi-structured. The classical approaches, techniques, tools and frameworks for management of data have become insufficient for processing this huge volume of data and not capable for providing efficient solution to handle the increased production of data. The major challenge in data mining of big data is, its inadequate approaches to analyze massive amount of online data (or data streams). Specially, the field of sentiment analysis and predictive analysis has become so much promising area to place an organization at doom or at boom by provide accurate decision at accurate time. The current paper provides an insight of machine learning algorithm both supervised and unsupervised method; and the traditional knowledge extraction process. The application field of sentiment analysis, the issues those are faced during data collection and cleaning. This study flourishes a complete picture of recommendation system based on the sentiment analysis of events. The key motivation of the paper is to incorporate the event sentiment analysis and give the feedback and recommendation and illustrate the ongoing researches in the field of sentiment analysis and its application.

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

Natural Language Processing ◽

10.4018/978-1-7998-0951-7.ch045 ◽

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.

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