scholarly journals A Framework for Arabic Concept-Level Sentiment Analysis using SenticNet

2018 ◽  
Vol 8 (5) ◽  
pp. 4015
Author(s):  
Hend G. Hassan ◽  
Hitham M. Abo Bakr ◽  
Ibrahim E. Ziedan
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Test Sample ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
The Internet ◽  
Slow Pace ◽  
Syntactic Features ◽  
Arabic Sentiment Analysis

Arabic Sentiment analysis research field has been progressing in a slow pace compared to English and other languages. In addition to that most of the contributions are based on using supervised machine learning algorithms while comparing the performance of different classifiers with different selected stylistic and syntactic features. In this paper, we presented a novel framework for using the Concept-level sentiment analysis approach which classifies text based on their semantics rather than syntactic features. Moreover, we provided a lexicon dataset of around 69 k unique concepts that covers multi-domain reviews collected from the internet. We also tested the lexicon on a test sample from the dataset it was collected from and obtained an accuracy of 70%. The lexicon has been made publicly available for scientific purposes.

scholarly journals Financial Context News Sentiment Analysis for the Lithuanian Language

2021 ◽  
Vol 11 (10) ◽  
pp. 4443
Author(s):  
Rokas Štrimaitis ◽  
Pavel Stefanovič ◽  
Simona Ramanauskaitė ◽  
Asta Slotkienė
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Experimental Investigations ◽  
Support Vector ◽  
Applied Machine Learning ◽  
Bayes Algorithm ◽  
Website Content

Financial area analysis is not limited to enterprise performance analysis. It is worth analyzing as wide an area as possible to obtain the full impression of a specific enterprise. News website content is a datum source that expresses the public’s opinion on enterprise operations, status, etc. Therefore, it is worth analyzing the news portal article text. Sentiment analysis in English texts and financial area texts exist, and are accurate, the complexity of Lithuanian language is mostly concentrated on sentiment analysis of comment texts, and does not provide high accuracy. Therefore in this paper, the supervised machine learning model was implemented to assign sentiment analysis on financial context news, gathered from Lithuanian language websites. The analysis was made using three commonly used classification algorithms in the field of sentiment analysis. The hyperparameters optimization using the grid search was performed to discover the best parameters of each classifier. All experimental investigations were made using the newly collected datasets from four Lithuanian news websites. The results of the applied machine learning algorithms show that the highest accuracy is obtained using a non-balanced dataset, via the multinomial Naive Bayes algorithm (71.1%). The other algorithm accuracies were slightly lower: a long short-term memory (71%), and a support vector machine (70.4%).

scholarly journals Implementation of Machine Learning Algorithms in Arabic Sentiment Analysis Using N-Gram Features

2019 ◽  
Vol 154 ◽  
pp. 332-340 ◽  
Author(s):  
Donia Gamal ◽  
Marco Alfonse ◽  
El-Sayed M. El-Horbaty ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
N Gram ◽  
Arabic Sentiment Analysis

scholarly journals Aspect Based Sentimental Analysis of Hotel Reviews: A Comparative Study

2020 ◽  
Vol 4 (1) ◽  
pp. 11-20
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Practical Significance ◽  
Support Vector ◽  
Use Of The Internet ◽  
And Task

The increasing use of the internet enables users to share their opinion about what they like and dislike regarding products and services. For efficient decision making, there is a need to analyze these reviews. Sentiment analysis or opinion mining is commonly used to detect polarity (positive or negative) of reviews. But, it does not show the aspect or orientation of the text. In this study, state-of-art approaches based on supervised machine learning employed to perform three tasks on the dataset provided by SemEval. Tasks A and B are related to predicting the aspect of the restaurant’s reviews, whereas task C shows their polarity. Additionally, this study aims to compare the performance of two feature engineering techniques and five machine learning algorithms to evaluate their performance on a publicly available dataset named SemEval-2015 Task 12. The experimental results showed that the word2vec features when used with the support vector machine algorithm outperformed by giving 76%, 72% and 79% off overall accuracies for Task A, Task B, and Task C respectively. Our comparative study holds practical significance and can be used as a baseline study in the domain of aspect-based sentiment analysis.

Classification of Sentiment of Reviews using Supervised Machine Learning Techniques

2017 ◽  
Vol 4 (1) ◽  
pp. 56-74 ◽  
Author(s):  
Abinash Tripathy ◽  
Santanu Kumar Rath
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Performance Parameters ◽  
Linear Discriminant ◽  
Learning Techniques

Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative or neutral. It is observed that very often the data associated with the sentiment analysis consist of the feedback given by various specialists on any topic or product. Thus, the review may be categorized properly into any sort of class based on the polarity, in order to have a good knowledge about the product. This article proposes an approach to classify the review dataset made on basis of sentiment analysis into different polarity groups. Four machine learning algorithms viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA) have been considered in this paper for classification process. The obtained result on values of accuracy of the algorithms are critically examined by using different performance parameters, applied on two different datasets.

scholarly journals Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic

2020 ◽  
Vol 11 (4) ◽  
pp. 33-45
Author(s):  
Omar Alharbi
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Positive Impact ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Linguistic Knowledge ◽  
Sentiment Lexicon ◽  
Colloquial Arabic ◽  
Arabic Sentiment Analysis ◽  
The Impact

One crucial aspect of sentiment analysis is negation handling, where the occurrence of negation can flip the sentiment of a review and negatively affects the machine learning-based sentiment classification. The role of negation in Arabic sentiment analysis has been explored only to a limited extent, especially for colloquial Arabic. In this paper, the authors address the negation problem in colloquial Arabic sentiment classification using the machine learning approach. To this end, they propose a simple rule-based algorithm for handling the problem that affects the performance of a machine learning classifier. The rules were crafted based on observing many cases of negation, simple linguistic knowledge, and sentiment lexicon. They also examine the impact of the proposed algorithm on the performance of different machine learning algorithms. Furthermore, they compare the performance of the classifiers when their algorithm is used against three baselines. The experimental results show that there is a positive impact on the classifiers when the proposed algorithm is used compared to the baselines.

Machine Learning in Sentiment Analysis Over Twitter

2021 ◽  
pp. 126-144
Author(s):  
Kadda Zerrouki
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
K Nearest Neighbor ◽  
Aspect Extraction ◽  
Different Levels ◽  
Document Level ◽  
F Measure

Social networks are the main resources to gather information about people's opinions and sentiments towards different topics as they spend hours daily on social media and share their opinions. Twitter is a platform widely used by people to express their opinions and display sentiments on different occasions. Sentiment analysis's (SA) task is to label people's opinions as different categories such as positive and negative from a given piece of text. Another task is to decide whether a given text is subjective, expressing the writer's opinions, or objective. These tasks were performed at different levels of analysis ranging from the document level to the sentence and phrase level. Another task is aspect extraction, which originated from aspect-based sentiment analysis in phrase level. All these tasks are under the umbrella of SA. In recent years, a large number of methods, techniques, and enhancements have been proposed for the problem of SA in different tasks at different levels. Sentiment analysis is an approach to analyze data and retrieve sentiment that it embodies. Twitter sentiment analysis is an application of sentiment analysis on data from Twitter (tweets) in order to extract sentiments conveyed by the user. In the past decades, the research in this field has consistently grown. The reason behind this is the challenging format of the tweets, which makes the processing difficult. The tweet format is very small, which generates a whole new dimension of problems like use of slang, abbreviations, etc. The chapter elaborately discusses three supervised machine learning algorithms—naïve Bayes, k-nearest neighbor (KNN), and decision tree—and compares their overall accuracy, precisions, as well as recall values; f-measure; number of tweets correctly classified; number of tweets incorrectly classified; and execution time.

Classification of Sentiment of Reviews using Supervised Machine Learning Techniques

2020 ◽  
pp. 143-163
Author(s):  
Abinash Tripathy ◽  
Santanu Kumar Rath
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Evaluation Report ◽  
Linear Discriminant ◽  
Learning Techniques

Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative or neutral. It is observed that very often the data associated with the sentiment analysis consist of the feedback given by various specialists on any topic or product. Thus, the review may be categorized properly into any sort of class based on the polarity, in order to have a good knowledge about the product. This article proposes an approach to classify the review dataset made on basis of sentiment analysis into different polarity groups. Four machine learning algorithms viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA) have been considered in this paper for classification process. The obtained result on values of accuracy of the algorithms are critically examined by using different performance parameters, applied on two different datasets.

A feature-centric spam email detection model using diverse supervised machine learning algorithms

2020 ◽  
Vol 38 (3) ◽  
pp. 633-657
Author(s):  
Ammara Zamir ◽  
Hikmat Ullah Khan ◽  
Waqar Mehmood ◽  
Tassawar Iqbal ◽  
Abubakker Usman Akram
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Research Study ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Content Type ◽  
Detection Model ◽  
Proposed Model

Purpose This research study proposes a feature-centric spam email detection model (FSEDM) based on content, sentiment, semantic, user and spam-lexicon features set. The purpose of this study is to exploit the role of sentiment features along with other proposed features to evaluate the classification accuracy of machine learning algorithms for spam email detection. Design/methodology/approach Existing studies primarily exploits content-based feature engineering approach; however, a limited number of features is considered. In this regard, this research study proposed a feature-centric framework (FSEDM) based on existing and novel features of email data set, which are extracted after pre-processing. Afterwards, diverse supervised learning techniques are applied on the proposed features in conjunction with feature selection techniques such as information gain, gain ratio and Relief-F to rank most prominent features and classify the emails into spam or ham (not spam). Findings Analysis and experimental results indicated that the proposed model with sentiment analysis is competitive approach for spam email detection. Using the proposed model, deep neural network applied with sentiment features outperformed other classifiers in terms of classification accuracy up to 97.2%. Originality/value This research is novel in this regard that no previous research focuses on sentiment analysis in conjunction with other email features for detection of spam emails.

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