A Fuzzy Logic Inspired Approach for Social Media Sentiment Analysis via Deep Neural Network

2018 ◽  
pp. 175-185 ◽  
Author(s):  
Anit Chakraborty ◽  
Anup Kolya ◽  
Sayandip Dutta
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Fuzzy Logic ◽  
Sentiment Analysis ◽  
Deep Neural Network ◽  
Media Sentiment

scholarly journals Deep Learning for Social Media Sentiment Analysis

MATEMATIKA ◽  
2020 ◽  
Vol 36 (2) ◽  
pp. 99-111
Author(s):  
Kartika Fithriasari ◽  
Saidah Zahrotul Jannah ◽  
Zakya Reyhana
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Characteristic Curve ◽  
Sentiment Classification ◽  
Radio Station ◽  
Learning Methods ◽  
Text Data ◽  
Media Sentiment

Social media is used as a tool by many people to express their opinions. Sentiment analysis for social media is very important, as it allows information to be obtained about public opinion on government performance. The goal of this research is to learn about the opinions of Surabaya citizens, using deep learning methods. The data are extracted from the official Twitter accounts of the Surabaya government and a private radio station in Surabaya. The data are grouped into two categories: positive and negative sentiments. This research is conducted in three steps: data pre-processing, sentiment classification, and visualization. Data pre-processing is required before modelling approaches are applied. It is used to transform the unstructured text data into structured data. The data pre-processing consists of case folding, tokenizing, and the removal of stop words. Deep learning methods are then applied to the data. A Backpropagation Neural Network (BNN) and a Convolutional Neural Network (CNN) are used to perform the sentiment classification. The BNN and CNN are compared using various metrics, such as precision, sensitivity, and area under the receiver operating characteristic curve (AUC). A word cloud is then used to visualize the data and find the most frequent words in each class. The results show that the sentiment classification with CNN is better than that with the BNN because the values for the precision, sensitivity and AUC are higher.

scholarly journals Social Media Sentiment Analysis: The Hajj Tweets Case Study

2021 ◽  
Vol 17 (3) ◽  
pp. 265-274
Author(s):  
Mohammad Ashraf Ottom ◽  
Khalid M.O. Nahar
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Media Sentiment

Sarcasm Detection Using Multi-Channel Attention Based BLSTM On News Headline

2020 ◽  
Author(s):  
Azika Syahputra Azwar ◽  
Suharjito
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Important Task ◽  
Hybrid Neural Network ◽  
Negative Opinion ◽  
Proposed Model ◽  
Short Memory ◽  
Literary Devices

Abstract Sarcasm is often used to express a negative opinion using positive or intensified positive words in social media. This intentional ambiguity makes sarcasm detection, an important task of sentiment analysis. Detecting a sarcastic tone in natural language hinders the performance of sentiment analysis tasks. The majority of the studies on automatic sarcasm detection emphasize on the use of lexical, syntactic, or pragmatic features that are often unequivocally expressed through figurative literary devices such as words, emoticons, and exclamation marks. In this paper, we introduce a multi-channel attention-based bidirectional long-short memory (MCAB-BLSTM) network to detect sarcastic headline on the news. Multi-channel attention-based bidirectional long-short memory (MCAB-BLSTM) proposed model was evaluated on the news headline dataset, and the results-compared to the CNN-LSTM and Hybrid Neural Network were excellent.

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