Multilingual Sentiment Classification on Large Textual Data

2014 ◽  
Author(s):  
Jantima Polpinij
Keyword(s):  
Sentiment Classification ◽  
Textual Data

scholarly journals Sentiment Classification Using Convolutional Neural Networks

2019 ◽  
Vol 9 (11) ◽  
pp. 2347 ◽  
Author(s):  
Hannah Kim ◽  
Young-Seob Jeong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Text Classification ◽  
State Of The Art ◽  
Sentiment Classification ◽  
Learning Models ◽  
Text Data ◽  
Textual Data ◽  
Better Than

As the number of textual data is exponentially increasing, it becomes more important to develop models to analyze the text data automatically. The texts may contain various labels such as gender, age, country, sentiment, and so forth. Using such labels may bring benefits to some industrial fields, so many studies of text classification have appeared. Recently, the Convolutional Neural Network (CNN) has been adopted for the task of text classification and has shown quite successful results. In this paper, we propose convolutional neural networks for the task of sentiment classification. Through experiments with three well-known datasets, we show that employing consecutive convolutional layers is effective for relatively longer texts, and our networks are better than other state-of-the-art deep learning models.

Sentiment Classification-How to Quantify Public Emotions Using Twitter

2018 ◽  
Vol 10 (1) ◽  
pp. 57-71 ◽  
Author(s):  
Prerna Mahajan ◽  
Anamika Rana
Keyword(s):  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Research Area ◽  
Sentiment Classification ◽  
Government Policies ◽  
Emotion Detection ◽  
Textual Data ◽  
Good Classification ◽  
Important Research Area ◽  
Sentiment Score

This article describes how with the tremendous popularity in the usage of social media has led to the explosive growth in unstructured data available on various social networking sites. Sentiment analysis of textual data collected from such platforms has become an important research area. In this article, the sentiment classification approach which employs an emotion detection technique is presented. To identify the emotions this paper uses the NRC lexicon based approach for identifying polarity of emotions. A score is computed to quantify emotions obtained from NRC lexicon approach. The method proposed has been tested on twitter datasets of government policies and reforms, more about current NDA government initiatives in India. The polarity components apply and classify the tweets into eight predefined emotions. This article performs both quantitative and sentiment analysis processes with the objective of analyzing the opinion conveyed to each social content, assign a category (+ve, -ve & neutral) or numbered sentiment score. The assigned scores have been classified using six different machine classification algorithms. Good classification results are achieved with the data.

scholarly journals TopFuzz4SA: An Integrated Fuzzy Neural Network with Topic-aware Auto-encoding for Sentiment Analysis

2021 ◽  
Author(s):  
Tham Vo
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Semantic Information ◽  
Contextual Information ◽  
Representation Learning ◽  
Sentiment Classification ◽  
Textual Data ◽  
Fuzzy Neural ◽  
Benchmark Datasets ◽  
Textual Representation

Abstract Recent advanced deep learning architectures, such as neural seq2seq, transformer, etc. have demonstrated remarkable improvements in multi-typed sentiment classification tasks. Even though recent transformer-based and seq2seq-based models have successfully enabled to capture rich-contextual information of texts, they are still lacking of attention on incorporating the global semantic information, such as topic, in order to sufficiently leverage the performance of downstream SA task. Moreover, emotional expressions of users are normally in forms of natural human-written textual data which might consist a lot of noise and ambiguity which impose great challenges on the processes of textual representation learning as well as sentiment polarity prediction. To meet these challenges, we propose a novel integrated fuzzy-neural architecture with a topic-driven textual representation learning approach for handling SA task, called as: TopFuzz4SA. Specifically, in the proposed TopFuzz4SA model, we first apply a topic-driven neural encoder-decoder architecture with the incorporation of latent topic embedding and attention mechanism to sufficiently learn both rich contextual and global semantic information of the given textual data. Then, the achieved rich semantic representations of texts are fed into a fused deep fuzzy neural network to effectively reduce the feature ambiguity and noise, forming the final textual representations for sentiment classification task. Extensive experiments in benchmark datasets demonstrate the effectiveness of our proposed TopFuzz4SA model in comparing with contemporary state-of-the-art baselines.

scholarly journals An Analysis of the Sentiment Classification of Short Messages Using Word2Vec

2018 ◽  
Author(s):  
Raul de Araújo Lima ◽  
Paulo T. Guerra
Keyword(s):  
Features Extraction ◽  
Sentiment Classification ◽  
Word Embeddings ◽  
Promising Tool ◽  
Textual Data ◽  
Good Classification ◽  
Polarity Classification ◽  
Short Messages

Sentiment analisys and the polarity classification of texts constitute one of the main tools currently used by companies and organizations for the most varied purposes. This work presents an analysis of the use of word embeddings, built through Word2Vec, in the process of features extraction for polarity classification of short messages written in English. The texts used were extracted from Twitter and the results obtained show that, in spite of the possible need to use larger textual bases to obtain better vectors, Word2Vec is a promising tool for the features extraction of textual data, contributing to obtain good classification results.

Patients’ Perceptions of Caring and Uncaring Nursing Encounters in Inpatient Rehabilitation Settings

2019 ◽  
Vol 21 (2) ◽  
Author(s):  
Joan C Cheruiyot ◽  
Petra Brysiewicz
Keyword(s):  
South Africa ◽  
Content Analysis ◽  
Inpatient Rehabilitation ◽  
Structured Interview ◽  
The Self ◽  
Personal Relationships ◽  
Individual Interviews ◽  
Textual Data ◽  
Interview Guide ◽  
Being There

This study explores and describes caring and uncaring nursing encounters from the perspective of the patients admitted to inpatient rehabilitation settings in South Africa. The researchers used an exploratory descriptive design. A semi-structured interview guide was used to collect data through individual interviews with 17 rehabilitation patients. Content analysis allowed for the analysis of textual data. Five categories of nursing encounters emerged from the analysis: noticing and acting, and being there for you emerged as categories of caring nursing encounters, and being ignored, being a burden, and deliberate punishment emerged as categories of uncaring nursing encounters. Caring nursing encounters make patients feel important and that they are not alone in the rehabilitation journey, while uncaring nursing encounters makes the patients feel unimportant and troublesome to the nurses. Caring nursing encounters give nurses an opportunity to notice and acknowledge the existence of vulnerability in the patients and encourage them to be present at that moment, leading to empowerment. Uncaring nursing encounters result in patients feeling devalued and depersonalised, leading to discouragement. It is recommended that nurses strive to develop personal relationships that promote successful nursing encounters. Further, nurses must strive to minimise the patients’ feelings of guilt and suffering, and to make use of tools, for example the self-perceived scale, to measure this. Nurses must also perform role plays on how to handle difficult patients such as confused, demanding and rude patients in the rehabilitation settings.

The So-Called Prothetic i- and the sḏm-f Paradigms II: The “Nominal” sḏm-f and a Reappraisal

2020 ◽  
Vol 28 ◽  
pp. 259-267
Author(s):  
Sami Uljas
Keyword(s):  
Present Author ◽  
Textual Data ◽  
Key Issues ◽  
Syllabic Structure ◽  
The Creation ◽  
Partial Revision

This article discusses, first, the role of the i-prefix in the so-called “nominal” sḏm-f paradigm in earliest Old Egyptian textual data. It is argued that this represented a means of facilitating the creation of a distinctive syllabic structure with 2rad roots and of harmonising it with that of the 2red and 3inf classes. Second, the study contains a partial revision of some of the key issues treated in an earlier article by the present author on the role of the similarly written prefix in the subjunctive and “circumstantial” sḏm-f paradigms.

Short Text Sentiment Classification Based on Context Reconstruction

2012 ◽  
Vol 38 (1) ◽  
pp. 55-67 ◽  
Author(s):  
Zhen YANG ◽  
Ying-Xu LAI ◽  
Li-Juan DUAN ◽  
Yu-Jian LI
Keyword(s):  
Sentiment Classification ◽  
Short Text
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