scholarly journals Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2739
Author(s):  
Fernando Andres Lovera ◽  
Yudith Coromoto Cardinale ◽  
Masun Nabhan Homsi
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Semantic Analysis ◽  
Hybrid Approach ◽  
Black Box ◽  
Machine Learning Techniques ◽  
Graph Similarity ◽  
Learning Techniques ◽  
Knowledge Graphs ◽  
N Gram

The traditional way to address the problem of sentiment classification is based on machine learning techniques; however, these models are not able to grasp all the richness of the text that comes from different social media, personal web pages, blogs, etc., ignoring the semantic of the text. Knowledge graphs give a way to extract structured knowledge from images and texts in order to facilitate their semantic analysis. This work proposes a new hybrid approach for Sentiment Analysis based on Knowledge Graphs and Deep Learning techniques to identify the sentiment polarity (positive or negative) in short documents, such as posts on Twitter. In this proposal, tweets are represented as graphs; then, graph similarity metrics and a Deep Learning classification algorithm are applied to produce sentiment predictions. This approach facilitates the traceability and interpretability of the classification results, thanks to the integration of the Local Interpretable Model-agnostic Explanations (LIME) model at the end of the pipeline. LIME allows raising trust in predictive models, since the model is not a black box anymore. Uncovering the black box allows understanding and interpreting how the network could distinguish between sentiment polarities. Each phase of the proposed approach conformed by pre-processing, graph construction, dimensionality reduction, graph similarity, sentiment prediction, and interpretability steps is described. The proposal is compared with character n-gram embeddings-based Deep Learning models to perform Sentiment Analysis. Results show that the proposal is able to outperforms classical n-gram models, with a recall up to 89% and F1-score of 88%.

scholarly journals Sentiment Analysis using various Machine Learning and Deep Learning Techniques

2021 ◽  
pp. 385-394
Author(s):  
V Umarani ◽  
A Julian ◽  
J Deepa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Process ◽  
Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

scholarly journals Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 4 ◽  
Author(s):  
Jurgita Kapočiūtė-Dzikienė ◽  
Robertas Damaševičius ◽  
Marcin Woźniak
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Full Dataset ◽  
Learning Techniques ◽  
Long Short Term Memory

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.

Deep Learning Approaches for Textual Sentiment Analysis

2020 ◽  
pp. 171-182 ◽  
Author(s):  
Tamanna Sharma ◽  
Anu Bajaj ◽  
Om Prakash Sangwan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Computational Technique ◽  
Learning Techniques

Sentiment analysis is computational measurement of attitude, opinions, and emotions (like positive/negative) with the help of text mining and natural language processing of words and phrases. Incorporation of machine learning techniques with natural language processing helps in analysing and predicting the sentiments in more precise manner. But sometimes, machine learning techniques are incapable in predicting sentiments due to unavailability of labelled data. To overcome this problem, an advanced computational technique called deep learning comes into play. This chapter highlights latest studies regarding use of deep learning techniques like convolutional neural network, recurrent neural network, etc. in sentiment analysis.

scholarly journals Winsorization for Robust Bayesian Neural Networks

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1546
Author(s):  
Somya Sharma ◽  
Snigdhansu Chatterjee
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Black Box ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Versatile Technique ◽  
Aberrant Observations ◽  
Probabilistic Machine Learning ◽  
Different Levels

With the advent of big data and the popularity of black-box deep learning methods, it is imperative to address the robustness of neural networks to noise and outliers. We propose the use of Winsorization to recover model performances when the data may have outliers and other aberrant observations. We provide a comparative analysis of several probabilistic artificial intelligence and machine learning techniques for supervised learning case studies. Broadly, Winsorization is a versatile technique for accounting for outliers in data. However, different probabilistic machine learning techniques have different levels of efficiency when used on outlier-prone data, with or without Winsorization. We notice that Gaussian processes are extremely vulnerable to outliers, while deep learning techniques in general are more robust.

Stance detection using diverse feature sets based on machine learning techniques

2021 ◽  
pp. 1-20
Author(s):  
Kashif Ayyub ◽  
Saqib Iqbal ◽  
Muhammad Wasif Nisar ◽  
Saima Gulzar Ahmad ◽  
Ehsan Ullah Munir
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Information Gain ◽  
Real Life ◽  
Machine Learning Techniques ◽  
Base Line ◽  
Feature Sets ◽  
Part Of Speech ◽  
Learning Techniques

 Sentiment analysis is the field that analyzes sentiments, and opinions of people about entities such as products, businesses, and events. As opinions influence the people’s behaviors, it has numerous applications in real life such as marketing, politics, social media etc. Stance detection is the sub-field of sentiment analysis. The stance classification aims to automatically identify from the source text, whether the source is in favor, neutral, or opposed to the target. This research study proposed a framework to explore the performance of the conventional (NB, DT, SVM), ensemble learning (RF, AdaBoost) and deep learning-based (DBN, CNN-LSTM, and RNN) machine learning techniques. The proposed method is feature centric and extracted the (sentiment, content, tweet specific and part-of-speech) features from both datasets of SemEval2016 and SemEval2017. The proposed study has also explored the role of deep features such as GloVe and Word2Vec for stance classification which has not received attention yet for stance detection. Some base line features such as Bag of words, N-gram, TF-IDF are also extracted from both datasets to compare the proposed features along with deep features. The proposed features are ranked using feature ranking methods such as (information gain, gain ration and relief-f). Further, the results are evaluated using standard performance evaluation measures for stance classification with existing studies. The calculated results show that the proposed feature sets including sentiment, (part-of-speech, content, and tweet specific) are helpful for stance classification when applied with SVM and GloVe a deep feature has given the best results when applied with deep learning method RNN.

Deep Learning Based Sentiment Analysis for Phishing SMS Detection

2021 ◽  
pp. 1-28
Author(s):  
Aakanksha Sharaff ◽  
Ramya Allenki ◽  
Rakhi Seth
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Models ◽  
Learning Techniques ◽  
Artificial Neural Network Ann ◽  
Baseline Algorithm

Sentiment analysis works on the principle of categorizing and identifying the text-based content and the process of classifying documents into one of the predefined classes commonly known as text classification. Hackers deploy a strategy by sending malicious content as an advertisement link and attack the user system to gain information. For protecting the system from this type of phishing attack, one needs to classify the spam data. This chapter is based on a discussion and comparison of various classification models that are used for phishing SMS detection through sentiment analysis. In this chapter, SMS data is collected from Kaggle, which is classified as ham or spam; while implementing the deep learning techniques like Convolutional Neural Network (CNN), CNN with 7 layers, and CNN with 11 layers, different results are generated. For evaluating these results, different machine learning techniques are used as a baseline algorithm like Naive Bayes, Decision Trees, Support Vector Machine (SVM), and Artificial Neural Network (ANN). After evaluation, CNN showed the highest accuracy of 99.47% as a classification model.

Sentiment Analysis by using deep learning and Machine learning Techniques: A Review

2021 ◽  
Vol 10 (2) ◽  
pp. 754-761
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Growing Up ◽  
Specific Subject ◽  
Learning Techniques ◽  
Day By Day

More and more individuals are now using online social networks and resources throughout this day and age to not only interact and to communicate but also for sharing their views, experiences, ideas, impression about anything. The analysis of sentiments is the identification and categorization of these views to evaluate public opinions on a specific subject, question, product, etc. Day by day, the relevance of sentiment analysis is growing up. Machine learning is an area or field of computer science where, without being specifically programmed, computers can learn. Deep learning is the part of machine learning and deals with the algorithm, which is most widely used as Neural network, neural belief, etc., in which neuronal implementations are considered. For sentiment analysis, it compares their performance and accuracy so then it can be inferred that deep learning techniques in most of the cases provide better results. The gap in the precision of these two approaches, however, is not as important enough in certain situations, and so it is best to apply and use the machine learning approaches and methods because these are simpler in terms of Implementation

Sign in / Sign up

Export Citation Format

Share Document