A Review on Multi-Lingual Sentiment Analysis by Machine Learning Methods
TLDR
This paper attempts to provide a detailed study on the sentiment analysis methods applied on languages other than English, covering methods that analyze translated data as well as methods that analyzed available data in the target language.About:
This article is published in Journal of Engineering Science and Technology Review.The article was published on 2020-04-01 and is currently open access. It has received 14 citations till now. The article focuses on the topics: Sentiment analysis.read more
Citations
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Journal ArticleDOI
An attention-based CNN-LSTM model for subjectivity detection in opinion-mining
TL;DR: This work presents an efficient subjectivity detection model for improved accuracy in Opinion-mining that uses a strategic combination of convolutional neural network and long short-term memory in an ensemble model.
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Zero-Shot Emotion Detection for Semi-Supervised Sentiment Analysis Using Sentence Transformers and Ensemble Learning
TL;DR: The emotion detection problem is addressed as part of the sentiment analysis task and a two-stage emotion detection methodology is proposed that achieves the highest accuracy of 87.3% on the English SemEval 2017 dataset.
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Improved method of word embedding for efficient analysis of human sentiments
TL;DR: A method of enhancing the performance of word embedding approaches, by integrating sentiment-based information, to render them more suitable for sentiment analysis is proposed.
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Development of a Multilingual Model for Machine Sentiment Analysis in the Serbian Language
TL;DR: The evaluation included applying developed models with three standard algorithms for classification problems (naive Bayes, logistic regression, and support vector machine) and applying a hybrid model, which produced the best results.
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Machine learning techniques for emotion detection and sentiment analysis: current state, challenges, and future directions
Alaa Alslaity,R. Orji +1 more
TL;DR: This article conducted a systematic review of 123 papers on machine learning-based emotion detection to investigate research trends along many themes, including machine learning approaches, application domain, data, evaluation, and outcome.
References
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Journal ArticleDOI
Lexicon-based methods for sentiment analysis
TL;DR: The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation, and is applied to the polarity classification task.
Journal ArticleDOI
Sentiment analysis algorithms and applications: A survey
TL;DR: This survey paper tackles a comprehensive overview of the last update in this field of sentiment analysis with sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas.
Journal ArticleDOI
A survey on opinion mining and sentiment analysis
Kumar Satish Ravi,Vadlamani Ravi +1 more
TL;DR: A rigorous survey on sentiment analysis is presented, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis.
Proceedings ArticleDOI
Co-Training for Cross-Lingual Sentiment Classification
TL;DR: A cotraining approach is proposed to making use of unlabeled Chinese data for cross-lingual sentiment classification, which leverages an available English corpus for Chinese sentiment classification by using the English corpus as training data.
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A machine learning approach to sentiment analysis in multilingual Web texts
Erik Boiy,Marie-Francine Moens +1 more
TL;DR: This paper presents machine learning experiments with regard to sentiment analysis in blog, review and forum texts found on the World Wide Web and written in English, Dutch and French and investigates the role of active learning techniques for reducing the number of examples to be manually annotated.