Sentiment Classification at the Time of the Tunisian Uprising: Machine Learning Techniques Applied to a New Corpus for Arabic Language

2014 ◽  
Author(s):  
Jalel Akaichi
Keyword(s):  
Machine Learning ◽  
Arabic Language ◽  
Sentiment Classification ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals A survey on sentiment classification algorithms, challenges and applications

2018 ◽  
Vol 10 (1) ◽  
pp. 58-72 ◽  
Author(s):  
Muhammad Rizwan Rashid Rana ◽  
Asif Nawaz ◽  
Javed Iqbal
Keyword(s):  
Machine Learning ◽  
Past Research ◽  
Sentiment Classification ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Comprehensive Overview ◽  
Survey Paper ◽  
The People ◽  
Learning Techniques ◽  
Excellent Research

Abstract Sentiment classification is the process of exploring sentiments, emotions, ideas and thoughts in the sentences which are expressed by the people. Sentiment classification allows us to judge the sentiments and feelings of the peoples by analyzing their reviews, social media comments etc. about all the aspects. Machine learning techniques and Lexicon based techniques are being mostly used in sentiment classification to predict sentiments from customers reviews and comments. Machine learning techniques includes several learning algorithms to judge the sentiments i.e Navie bayes, support vector machines etc whereas Lexicon Based techniques includes SentiWordnet, Wordnet etc. The main target of this survey is to give nearly full image of sentiment classification techniques. Survey paper provides the comprehensive overview of recent and past research on sentiment classification and provides excellent research queries and approaches for future aspects

Application of Optimization and Machine Learning for Sentiment Analysis

2021 ◽  
pp. 1-7
Author(s):  
Manitosh Chourasiya ◽  
Prof. Devendra Singh Rathod
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Algorithm ◽  
Sentiment Classification ◽  
Machine Learning Techniques ◽  
Assessment Scores ◽  
Opinion Extraction ◽  
Learning Techniques ◽  
High Level

Sentiment analysis is called detecting emotions extracted from text features and is known as one of the most important parts of opinion extraction. Through this process, we can determine if a script is positive, negative or neutral. In this research, sentiment analysis is performed with textual data. A text feeling analyzer combines natural language processing (NLP) and machine learning techniques to assign weighted assessment scores to entities, subjects, subjects, and categories within a sentence or phrase. In expressing mood, the polarity of text reviews could be graded on a negative to positive scale using a learning algorithm. The current decade has seen significant developments in artificial intelligence, and the machine learning revolution has changed the entire AI industry. After all, machine learning techniques have become an integral part of any model in today's computing world. However, the ensemble to learning techniques is promise a high level of automation with the extraction of generalized rules for text and sentiment classification activities. This thesis aims to design and implement an optimized functionality matrix using to the ensemble learning for the sentiment classification and its applications.

scholarly journals Sentiment Analysis Techniques and Application-Survey and Taxonomy

2021 ◽  
Vol 04 (01) ◽  
Author(s):  
Mahmood Umar ◽  
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Sentiment Classification ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Generation ◽  
Analysis Techniques ◽  
Learning Techniques ◽  
Document Level

Nowadays, social media platforms, blogs, and e-commerce are commonly use to express opinion on politics, movies, products, education respectively; for election forecasting, business boosting and improvement of teaching and learning. As a result, data generation becomes easier; producing big data which requires appropriate techniques and tools to analyse easily, accurately and timely. Thus, making sentiment analysis very demanding research area. This study will investigate on what basis (sentiment classification level) or area of application (data source) do supervised machine learning approaches particularly Support Vector Machine (SVM), Naïve Bayes, and Maximum Entropy algorithms, and other technique-lexicon-based approach give the best result in sentiment analysis. Based on the review of the literature there is a contradiction on the point that SVM generated the best result in analyzing student sentiment on document level. This study also discovers that sentiment analysis differs from system to system based on polarity (types of the classes to predict: positive or negative, subjective or objective), different levels of classification (sentence, phrase, or document level) and language that is processed. This research produces a taxonomy which serves as a guide for the choice of techniques in sentiment analysis. The taxonomy explores the sentiment classification levels and data preprocessing stages. It also explores that sentiment analysis techniques were organised in to three (3) groups; Machine learning, Lexicon and hybrid or combination. The machine learning techniques were sub-grouped in to two (2) namely; supervised and unsupervised. The supervised were organized in to two (2): Classification and Regression. un-supervised machine learning techniques includes clustering and association. The clustering technique consist of k-means. Decision tree which is a classification based under supervised type of machine learning technique consist of random forest,(Akinkunmi, 2019) while the ruled-based classifiers consist of confidence criterion and support criterion. The commonly used tools are Weka, Python compiler, and R programming tool.

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