scholarly journals Ensemble Sentiment Analysis Method based on R-CNN and C-RNN with Fusion Gate

2019 ◽  
Vol 14 (2) ◽  
pp. 272-285 ◽  
Author(s):  
Fushen Yang ◽  
Changshun Du ◽  
Lei Huang
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Contextual Information ◽  
Final Analysis ◽  
Service Evaluation ◽  
Feature Engineering ◽  
Data Sets ◽  
Analysis Method ◽  
Analysis Model ◽  
Monitoring Service

Text sentiment analysis is one of the most important tasks in the field of public opinion monitoring, service evaluation and satisfaction analysis in the current network environment. At present, the sentiment analysis algorithms with good effects are all based on statistical learning methods. The performance of this method depends on the quality of feature extraction, while good feature engineering requires a high degree of expertise and is also time-consuming, laborious, and affords poor opportunities for mobility. Neural networks can reduce dependence on feature engineering. Recurrent neural networks can obtain context information but the order of words will lead to bias; the text analysis method based on convolutional neural network can obtain important features of text through pooling but it is difficult to obtain contextual information. Aiming at the above problems, this paper proposes a sentiment analysis method based on the combination of R-CNN and C-RNN based on a fusion gate. Firstly, RNN and CNN are combined in different ways to alleviate the shortcomings of the two, and the sub-analysis network R-CNN and C-RNN finally combine the two networks through the gating unit to form the final analysis model. We performed experiments on different data sets to verify the effectiveness of the method.

scholarly journals TEXT SENTIMENT ANALYSIS BASED ON CNNS AND SVM

2019 ◽  
Vol 7 (6) ◽  
pp. 77-83 ◽  
Author(s):  
Dr. C. Arunabala ◽  
P. Jwalitha ◽  
Soniya Nuthalapati
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Sentiment Analysis ◽  
Expressive Power ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Analysis Method ◽  
Mapping Functions ◽  
Generalization Ability ◽  
Text Sentiment Analysis

The traditional text sentiment analysis method is mainly based on machine learning. However, its dependence on emotion dictionary construction and artificial design and extraction features makes the generalization ability limited. In contrast, depth models have more powerful expressive power, and can learn complex mapping functions from data to affective semantics better. In this paper, a Convolution Neural Networks (CNNs) model combined with SVM text sentiment analysis is proposed. The experimental results show that the proposed method improves the accuracy of text sentiment classification effectively compared with traditional CNN, and confirms the effectiveness of sentiment analysis based on CNNs and SVM

scholarly journals Sentiment Polarity Detection in Bengali Tweets Using Deep Convolutional Neural Networks

2019 ◽  
Vol 28 (3) ◽  
pp. 377-386 ◽  
Author(s):  
Kamal Sarkar
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Convolutional Neural Networks ◽  
Early Stage ◽  
Contextual Information ◽  
Indian Languages ◽  
Deep Convolutional Neural Networks ◽  
Detection Model ◽  
Detection Systems ◽  
Hidden Layer

Abstract Sentiment polarity detection is one of the most popular sentiment analysis tasks. Sentiment polarity detection in tweets is a more difficult task than sentiment polarity detection in review documents, because tweets are relatively short and they contain limited contextual information. Although the amount of blog posts, tweets and comments in Indian languages is rapidly increasing on the web, research on sentiment analysis in Indian languages is at the early stage. In this paper, we present an approach that classifies the sentiment polarity of Bengali tweets using deep neural networks which consist of one convolutional layer, one hidden layer and one output layer, which is a soft-max layer. Our proposed approach has been tested on the Bengali tweet dataset released for Sentiment Analysis in Indian Languages contest 2015. We have compared the performance of our proposed convolutional neural networks (CNN)-based model with a sentiment polarity detection model that uses deep belief networks (DBN). Our experiments reveal that the performance of our proposed CNN-based system is better than our implemented DBN-based system and some existing Bengali sentiment polarity detection systems.

Neural Networks with Emotion Associations, Topic Modeling and Supervised Term Weighting for Sentiment Analysis

2021 ◽  
pp. 2150013
Author(s):  
Petr Hajek ◽  
Aliaksandr Barushka ◽  
Michal Munk
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Word Embeddings ◽  
Document Representation ◽  
Analysis Model ◽  
Term Weighting ◽  
Machine Learning Methods ◽  
Proposed Model

Automated sentiment analysis is becoming increasingly recognized due to the growing importance of social media and e-commerce platform review websites. Deep neural networks outperform traditional lexicon-based and machine learning methods by effectively exploiting contextual word embeddings to generate dense document representation. However, this representation model is not fully adequate to capture topical semantics and the sentiment polarity of words. To overcome these problems, a novel sentiment analysis model is proposed that utilizes richer document representations of word-emotion associations and topic models, which is the main computational novelty of this study. The sentiment analysis model integrates word embeddings with lexicon-based sentiment and emotion indicators, including negations and emoticons, and to further improve its performance, a topic modeling component is utilized together with a bag-of-words model based on a supervised term weighting scheme. The effectiveness of the proposed model is evaluated using large datasets of Amazon product reviews and hotel reviews. Experimental results prove that the proposed document representation is valid for the sentiment analysis of product and hotel reviews, irrespective of their class imbalance. The results also show that the proposed model improves on existing machine learning methods.

Sentiment Recognition in Customer Reviews Using Deep Learning

2020 ◽  
pp. 1383-1393
Author(s):  
Vinay Kumar Jain ◽  
Shishir Kumar ◽  
Prabhat Kumar Mahanti
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Customer Reviews ◽  
Weakly Supervised ◽  
Comparison Of The Results

Deep learning has become popular in all aspect related to human judgments. Most machine learning techniques work well which includes text classification, text sequence learning, sentiment analysis, question-answer engine, etc. This paper has been focused on two objectives, firstly is to study the applicability of deep neural networks strategies for extracting sentiment present in social media data and customer reviews with effective training solutions. The second objective is to design deep networks that can be trained with these weakly supervised strategies in order to predict meaningful inferences. This paper presents the concept and steps of using deep learning for extraction sentiments from customer reviews. The extraction pulls out the features from the customer reviews using deep learning popular methods including Convolution neural networks (CNN) and Long Short-Term Memory (LSTM) architectures. The comparison of the results with tradition text classification method such as Naive Bayes(NB) and Support Vector Machine(SVM) using two data sets IMDB reviews and Amazon customer reviews have been presented. This work mainly focused on investigating the merit of using deep models for sentiment analysis in customer reviews.

Management of Guidance and Counseling on Learning Discipline

2020 ◽  
Vol 10 (2) ◽  
pp. 60-74
Author(s):  
Muhammad Syauqi Mubarok
Keyword(s):  
Statistical Analysis ◽  
Descriptive Analysis ◽  
Field Studies ◽  
Guidance And Counseling ◽  
Analysis Method ◽  
Vocational High School ◽  
Analysis Model ◽  
Survey Techniques ◽  
Analysis Technique ◽  
Path Analysis Model

This article aims to examine and describe the influence of guidance and counseling management on learning discipline. The method used in this research is descriptive analysis method using survey techniques. Data collection techniques that used are documentation studies and field studies. Moreover, the data analysis technique that has been used to answer the research hypothesis is statistical analysis with a path analysis model. The location of the study was at the Ciledug Vocational High School Al-Musaddadiyah Garut, with 85 respondents taking part in the survey. The results of the discussion show that guidance and counseling management has a positive and significant effect on the discipline of learning

Learning emotional word embeddings for sentiment analysis

2021 ◽  
pp. 1-13
Author(s):  
Qingtian Zeng ◽  
Xishi Zhao ◽  
Xiaohui Hu ◽  
Hua Duan ◽  
Zhongying Zhao ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Research Problem ◽  
Emotional Word ◽  
Classification Model ◽  
Data Sets ◽  
Word Embeddings ◽  
Real World Data ◽  
Text Documents

Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.

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