Intelligent sentinet-based lexicon for context-aware sentiment analysis: optimized neural network for sentiment classification on social media

2021 ◽  
Author(s):  
K. E. Naresh Kumar ◽  
V. Uma
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Sentiment Analysis ◽  
Sentiment Classification ◽  
Context Aware

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.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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.

Predicting the French Stock Market Using Social Media Analysis

2015 ◽  
Vol 7 (2) ◽  
pp. 70-84
Author(s):  
Vincent Martin ◽  
Emmanuel Bruno ◽  
Elisabeth Murisasco
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Percentage Error ◽  
Social Media Analysis ◽  
Single Output ◽  
The Neural Network ◽  
Simple Neural Network

In this article, the authors try to predict the next-day CAC40 index. They apply the idea of Johan Bollen et al. from (Bollen, Mao, & Zeng, 2011) on the French stock market and they conduct their experiment using French tweets. Two analyses are applied on tweets: sentiment analysis and subjectivity analysis. Results of these analyses are then used to train a simple neural network. The input features are the sentiment, the subjectivity and the CAC40 closing value at day-1 and day-0. The single output value is the predicted CAC40 closing value at day+1. The authors propose an architecture using the JEE framework resulting in a better scalability and an easier industrialization. The main experiments are conducted over 5 months of data. The authors train their neural network on the first of the data and they test predictions on the remaining quarter. Their best run gives a direction accuracy of 80% and a mean absolute percentage error (MAPE) of 2.97%. In another experiment, the authors retrain the neural network each day which decreases the MAPE to 1.14%.

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.

scholarly journals IMPLEMENTATION OF BACKPROPAGATION NEURAL NETWORK IN SENTIMENT ANALYSIS ON TWITTER TO PUBLIC FIGURES

Compiler ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 101
Author(s):  
Achmad Safruddin ◽  
Arief Hermawan ◽  
Adityo Permana Wibowo
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Sentiment Analysis ◽  
Test Data ◽  
Text Processing ◽  
Correct Classification ◽  
Backpropagation Neural Network ◽  
Public Figures ◽  
Twitter Users ◽  
Topic Sentiment

Sentiment analysis is a process for identifying or analyzing people's opinions on a topic. Sentiment analysis analyzes each word in a sentence to find out the opinions or sentiments expressed in the sentence. The opinions expressed can be in the form of positive or negative opinions. Twitter is one of the most popular social media in Indonesia. Twitter users always discuss various kinds of topics every day. One of the things discussed on Twitter and which has become a trending topic several times is about public figures. This study discusses the analysis of positive or negative sentiments towards public figures based on tweet data carried out by text processing. The results of text processing are classified using a backpropagation neural network. Tests were carried out using 69 test data, resulting in an accuracy of 62.3%, with 43 correct classification results.

scholarly journals Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

2020 ◽  
Author(s):  
Hamed Jelodar ◽  
Yongli Wang ◽  
Rita Orji ◽  
Hucheng Huang
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Recurrent Neural Network ◽  
Online Discussions ◽  
Sentiment Classification ◽  
World Health ◽  
Computational Techniques ◽  
Public Opinions ◽  
The World ◽  
Novel Coronavirus

AbstractInternet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19–related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making.

scholarly journals Context-aware social media user sentiment analysis

2020 ◽  
Vol 25 (4) ◽  
pp. 528-541 ◽  
Author(s):  
Bo Liu ◽  
Shijiao Tang ◽  
Xiangguo Sun ◽  
Qiaoyun Chen ◽  
Jiuxin Cao ◽  
...  
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Context Aware

scholarly journals Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiaodi Wang ◽  
Xiaoliang Chen ◽  
Mingwei Tang ◽  
Tian Yang ◽  
Zhen Wang
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Network Models ◽  
Attention Mechanism ◽  
Sentiment Classification ◽  
Classification Model ◽  
Neural Network Models ◽  
Position Information ◽  
Gated Recurrent Unit ◽  
Relative Position Information

The aim of aspect-level sentiment analysis is to identify the sentiment polarity of a given target term in sentences. Existing neural network models provide a useful account of how to judge the polarity. However, context relative position information for the target terms is adversely ignored under the limitation of training datasets. Considering position features between words into the models can improve the accuracy of sentiment classification. Hence, this study proposes an improved classification model by combining multilevel interactive bidirectional Gated Recurrent Unit (GRU), attention mechanisms, and position features (MI-biGRU). Firstly, the position features of words in a sentence are initialized to enrich word embedding. Secondly, the approach extracts the features of target terms and context by using a well-constructed multilevel interactive bidirectional neural network. Thirdly, an attention mechanism is introduced so that the model can pay greater attention to those words that are important for sentiment analysis. Finally, four classic sentiment classification datasets are used to deal with aspect-level tasks. Experimental results indicate that there is a correlation between the multilevel interactive attention network and the position features. MI-biGRU can obviously improve the performance of classification.

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