Sentiment Analysis of Covid Vaccine Tweets Using Different Text Classification Models

2021 ◽  
pp. 231-242
Author(s):  
R. Rahul ◽  
C. S. Aravind ◽  
T. Remya Nair
Keyword(s):  
Sentiment Analysis ◽  
Text Classification ◽  
Classification Models

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

Sentiment Analysis of Text Classification Using RNN Algorithm

2021 ◽  
pp. 561-571
Author(s):  
Gitashree Borah ◽  
Dipika Nimje ◽  
G. JananiSri ◽  
K. P. Bharath ◽  
M. Rajesh Kumar
Keyword(s):  
Sentiment Analysis ◽  
Text Classification

scholarly journals GILE: A Generalized Input-Label Embedding for Text Classification

2019 ◽  
Vol 7 ◽  
pp. 139-155 ◽  
Author(s):  
Nikolaos Pappas ◽  
James Henderson
Keyword(s):  
Text Classification ◽  
Joint Space ◽  
Classification Performance ◽  
Cross Entropy ◽  
Categorical Variables ◽  
Classification Models ◽  
Set Size ◽  
Nonlinear Input ◽  
Label Semantics

Neural text classification models typically treat output labels as categorical variables that lack description and semantics. This forces their parametrization to be dependent on the label set size, and, hence, they are unable to scale to large label sets and generalize to unseen ones. Existing joint input-label text models overcome these issues by exploiting label descriptions, but they are unable to capture complex label relationships, have rigid parametrization, and their gains on unseen labels happen often at the expense of weak performance on the labels seen during training. In this paper, we propose a new input-label model that generalizes over previous such models, addresses their limitations, and does not compromise performance on seen labels. The model consists of a joint nonlinear input-label embedding with controllable capacity and a joint-space-dependent classification unit that is trained with cross-entropy loss to optimize classification performance. We evaluate models on full-resource and low- or zero-resource text classification of multilingual news and biomedical text with a large label set. Our model outperforms monolingual and multilingual models that do not leverage label semantics and previous joint input-label space models in both scenarios.

scholarly journals Identification of Judicial Outcomes in Judgments: A Generalized Gini-PLS Approach

Stats ◽  
2020 ◽  
Vol 3 (4) ◽  
pp. 427-443
Author(s):  
Gildas Tagny-Ngompé ◽  
Stéphane Mussard ◽  
Guillaume Zambrano ◽  
Sébastien Harispe ◽  
Jacky Montmain
Keyword(s):  
Law Enforcement ◽  
Success Rate ◽  
Text Classification ◽  
Binary Classification ◽  
Classification Algorithms ◽  
Classification Models ◽  
Target Variable ◽  
Technical Aspects ◽  
Legal Documents ◽  
Judicial Outcomes

This paper presents and compares several text classification models that can be used to extract the outcome of a judgment from justice decisions, i.e., legal documents summarizing the different rulings made by a judge. Such models can be used to gather important statistics about cases, e.g., success rate based on specific characteristics of cases’ parties or jurisdiction, and are therefore important for the development of Judicial prediction not to mention the study of Law enforcement in general. We propose in particular the generalized Gini-PLS which better considers the information in the distribution tails while attenuating, as in the simple Gini-PLS, the influence exerted by outliers. Modeling the studied task as a supervised binary classification, we also introduce the LOGIT-Gini-PLS suited to the explanation of a binary target variable. In addition, various technical aspects regarding the evaluated text classification approaches which consists of combinations of representations of judgments and classification algorithms are studied using an annotated corpora of French justice decisions.

Customized reputation generation of entities using sentiment analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arpita Gupta ◽  
Saloni Priyani ◽  
Ramadoss Balakrishnan
Keyword(s):  
Sentiment Analysis ◽  
Classification Models ◽  
Data Set ◽  
Content Type ◽  
Customer Reviews ◽  
Art Methods ◽  
Review Helpfulness ◽  
Sentiment Score ◽  
State Of Art

Purpose In this study, the authors have used the customer reviews of books and movies in natural language for the purpose of sentiment analysis and reputation generation on the reviews. Most of the existing work has performed sentiment analysis and reputation generation on the reviews by using single classification models and considered other attributes for reputation generation. Design/methodology/approach The authors have taken review, helpfulness and rating into consideration. In this paper, the authors have performed sentiment analysis for extracting the probability of the review belonging to a class, which is further used for generating the sentiment score and reputation of the review. The authors have used pre-trained BERT fine-tuned for sentiment analysis on movie and book reviews separately. Findings In this study, the authors have also combined the three models (BERT, Naïve Bayes and SVM) for more accurate sentiment classification and reputation generation, which has outperformed the best BERT model in this study. They have achieved the best accuracy of 91.2% for the movie review data set and 89.4% for the book review data set which is better than the existing state-of-art methods. They have used the transfer learning concept in deep learning where you take knowledge gained from one problem and apply it to a similar problem. Originality/value The authors have proposed a novel model based on combination of three classification models, which has outperformed the existing state-of-art methods. To the best of the authors’ knowledge, there is no existing model which combines three models for sentiment score calculation and reputation generation for the book review data set.

scholarly journals A Survey of Arabic Text Classification Models

2018 ◽  
Vol 8 (6) ◽  
pp. 4352 ◽  
Author(s):  
Ahed M. F. Al-Sbou
Keyword(s):  
Language Processing ◽  
Text Classification ◽  
Arabic Language ◽  
Arabic Text ◽  
Classification Models ◽  
Natural Languages ◽  
Text Organization ◽  
Arabic Text Classification ◽  
Arabic Language Processing

<p>There is a huge content of Arabic text available over online that requires an organization of these texts. As result, here are many applications of natural languages processing (NLP) that concerns with text organization. One of the is text classification (TC). TC helps to make dealing with unorganized text. However, it is easier to classify them into suitable class or labels. This paper is a survey of Arabic text classification. Also, it presents comparison among different methods in the classification of Arabic texts, where Arabic text is represented a complex text due to its vocabularies. Arabic language is one of the richest languages in the world, where it has many linguistic bases. The researche in Arabic language processing is very few compared to English. As a result, these problems represent challenges in the classification, and organization of specific Arabic text. Text classification (TC) helps to access the most documents, or information that has already classified into specific classes, or categories to one or more classes or categories. In addition, classification of documents facilitate search engine to decrease the amount of document to, and then to become easier to search and matching with queries.</p>

scholarly journals PERFECTIONOF CLASSIFICATION ACCURACY IN TEXT CATEGORIZATION

2021 ◽  
Vol 9 (09) ◽  
pp. 484-488
Author(s):  
Rajeev Tripathi ◽  
Keyword(s):  
Sentiment Analysis ◽  
Text Classification ◽  
Classification Accuracy ◽  
Text Categorization ◽  
Classification Model ◽  
Text Data ◽  
Twitter Data ◽  
Long Time ◽  
Google Alerts ◽  
Email Spam

Problems and strategies for text classification have already been known for a long time. Theyre widely utilised by companies like Google and Yahoo for email spam screening, sentiment analysis of Twitter data, and automatic news categories in Google alerts. Were still working on getting the findings to be as accurate as possible. When dealing with large amounts of text data, however, the models performance and accuracy become a difficulty. The type of words utilised in the corpus and the type of features produced for classification have a big impact on the performance of a text classification model.

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