scholarly journals Twitter Sentiment Analysis

2018 ◽  
Vol 7 (12) ◽  
pp. 15
Author(s):  
Harshil Shah
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Hybrid Approach ◽  
Public Opinions ◽  
The One

With the increasing popularity of social media, people have begun to express their opinions on a variety of topics on Twitter and other similar services.Sentiment Analysis on tweets has gained much attention for gathering public opinions on a wide variety of topics. In this paper, we aim to tackle the one of the fundamental problems of sentiment analysis, sentiment polarity categorization. We present a hybrid approach for identifying sentiments from a given piece of text.

Hybrid Approach for Sentiment Analysis of Twitter Posts Using a Dictionary-based Approach and Fuzzy Logic Methods

2020 ◽  
Vol 16 (1) ◽  
pp. 116-145 ◽  
Author(s):  
Jamilah Rabeh Alharbi ◽  
Wadee S. Alhalabi
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Fuzzy Systems ◽  
Fuzzy Inference ◽  
Service Providers ◽  
Hybrid Approach ◽  
Cloud Service ◽  
Inference Process ◽  
Huge Amount ◽  
Fuzzy Parameters

Recently, sentiment analysis of social media has become a hot topic because of the huge amount of information that is provided in these networks. Twitter is a popular social media application offers businesses and government the opportunities to share and acquire information. This article proposes a technique that aims at measuring customers' satisfaction with cloud service providers, based on their tweets. Existing techniques focused on classifying sentimental text as either positive or negative, while the proposed technique classifies the tweets into five categories to provide better information. A hybrid approach of dictionary-based and Fuzzy Inference Process (FIP) is developed for this purpose. This direction was selected for its advantages and flexibility in addressing complex problems, using terms that reflect on human behaviors and experiences. The proposed hybrid-based technique used fuzzy systems in order to accurately identify the sentiment of the input text while addressing the challenges that are facing sentiment analysis using various fuzzy parameters.

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.

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

2020 ◽  
pp. 879-896
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.

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

2017 ◽  
Vol 13 (1) ◽  
pp. 11-27 ◽  
Author(s):  
Youjia Fang ◽  
Xin Chen ◽  
Zheng Song ◽  
Tianzi Wang ◽  
Yang Cao
Keyword(s):  
Social Media ◽  
Big Data ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Information Diffusion ◽  
Compartmental Model ◽  
Model Fitting ◽  
Compartmental Models ◽  
Sina Weibo ◽  
Public Opinions

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.

An automated learning model for sentiment analysis and data classification of Twitter data using balanced CA-SVM

2021 ◽  
pp. 1063293X2110314
Author(s):  
C Pretty Diana Cyril ◽  
J Rene Beulah ◽  
Neelakandan Subramani ◽  
Prakash Mohan ◽  
A Harshavardhan ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Modern Society ◽  
Support Vector ◽  
Analysis Model ◽  
Data Set ◽  
Twitter Data ◽  
Automated Learning ◽  
Sentiment Score ◽  
The One

The modern society runs over the social media for their most time of every day. The web users spend their most time in social media and they share many details with their friends. Such information obtained from their chat has been used in several applications. The sentiment analysis is the one which has been applied with Twitter data set toward identifying the emotion of any user and based on those different problems can be solved. Primarily, the data as of the Twitter database is preprocessed. In this step, tokenization, stemming, stop word removal, and number removal are done. The proposed automated learning with CA-SVM based sentiment analysis model reads the Twitter data set. After that they have been processed to extract the features which yield set of terms. Using the terms, the tweets are clustered using TGS-K means clustering which measures Euclidean distance according to different features like semantic sentiment score (SSS), gazetteer and symbolic sentiment support (GSSS), and topical sentiment score (TSS). Further, the method classifies the tweets according to support vector machine (CA-SVM) which classifies the tweet according to the support value which is measured based on the above two measures. The attained results are validated utilizing k-fold cross-validation methodology. Then, the classification is performed by utilizing the Balanced CA-SVM (Deep Learning Modified Neural Network). The results are evaluated and compared with the existing works. The Proposed model achieved 92.48 % accuracy and 92.05% sentiment score contrasted with the existing works.

A Survey on Implementation Methods and Applications of Sentiment Analysis

2019 ◽  
pp. 44-58
Author(s):  
Sudheer Karnam ◽  
Valarmathi B. ◽  
Tulasi Prasad Sariki
Keyword(s):  
Social Media ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Mobile Phones ◽  
Opinion Mining ◽  
Classification Methods ◽  
Analysis Problem ◽  
Public Opinions ◽  
The Public ◽  
Research Problems

Sentiment analysis also called opinion mining, and it studies opinions of people towards products and services. Opinions are very important as the organizations always want to know the public opinions about their products and services. People give their opinions via social media. With the advent of social media like Twitter, Facebook, blogs, forums, etc. sentiment analysis has become important in every field like automobile, medical, film, fashion, stock market, mobile phones, insurance, etc. Analyzing the opinions and predicting the opinion is called sentiment analysis. Sentiment analysis is done using opinion words by classification methods or by sentiment lexicons. This chapter compares different methods of solving sentiment analysis problem, algorithms, its merits and demerits, applications, and also investigates different research problems in sentiment analysis.

scholarly journals A supervised scheme for aspect extraction in sentiment analysis using the hybrid feature set of word dependency relations and lemmas

2021 ◽  
Vol 7 ◽  
pp. e347
Author(s):  
Bhavana R. Bhamare ◽  
Jeyanthi Prabhu
Keyword(s):  
Social Media ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Hybrid Approach ◽  
Structured Data ◽  
Unstructured Data ◽  
Aspect Extraction ◽  
Dependency Relation ◽  
Dependency Relations ◽  
The Web

Due to the massive progression of the Web, people post their reviews for any product, movies and places they visit on social media. The reviews available on social media are helpful to customers as well as the product owners to evaluate their products based on different reviews. Analyzing structured data is easy as compared to unstructured data. The reviews are available in an unstructured format. Aspect-Based Sentiment Analysis mines the aspects of a product from the reviews and further determines sentiment for each aspect. In this work, two methods for aspect extraction are proposed. The datasets used for this work are SemEval restaurant review dataset, Yelp and Kaggle datasets. In the first method a multivariate filter-based approach for feature selection is proposed. This method support to select significant features and reduces redundancy among selected features. It shows improvement in F1-score compared to a method that uses only relevant features selected using Term Frequency weight. In another method, selective dependency relations are used to extract features. This is done using Stanford NLP parser. The results gained using features extracted by selective dependency rules are better as compared to features extracted by using all dependency rules. In the hybrid approach, both lemma features and selective dependency relation based features are extracted. Using the hybrid feature set, 94.78% accuracy and 85.24% F1-score is achieved in the aspect category prediction task.

scholarly journals ANALISIS SENTIMEN TOPIK VIRAL DESA PENARI PADA MEDIA SOSIAL TWITTER DENGAN METODE LEXICON BASED

2019 ◽  
Vol 21 (3) ◽  
pp. 242-250
Author(s):  
Rifiana Arief ◽  
Karel Imanuel
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Sentiment Analysis ◽  
Sentiment Classification ◽  
Public Opinions ◽  
Real Work ◽  
Horror Story ◽  
Assessment Results

Abstract :  The horror story of Dancer Village in Indonesia is a viral topic that has become a talk of citizens on Twitter social media. Various responses and public opinions emerged related to the truth of the story of supernatural experiences of students during a Real Work Lecture in an East Java region of Indonesia. This study conducted a sentiment analysis of community comments on Twitter social media on the viral topic using the Lexicon Based method. Sentiment classification is divided into 3 classes namely positive, negative and neutral. The research phase consists of data collection, pre-processing, processing (sentiment analysis) and visualization. Data collection uses Twitter Search API with 1000 Penari Desa keywords in Indonesian. The lexicon assessment results from 1000 tweets data obtained 33 positive, 767 neutral and 200 negative. The percentage of tweets containing positive comments by 3.3%, neutral 76.7% and negative by 20%

Hybrid Approach to Sentiment Analysis based on Syntactic Analy- sis and Machine Learning

2010 ◽  
Vol 14 (2) ◽  
pp. 159-181
Author(s):  
MUNPYO HONG ◽  
MIYOUNG SHIN ◽  
Shinhye Park ◽  
Hyungmin Lee
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Hybrid Approach
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