scholarly journals Sentiment analysis of informal Malay tweets with deep learning

2020 ◽  
Vol 9 (2) ◽  
pp. 212
Author(s):  
Ong Jun Ying ◽  
Muhammad Mun'im Ahmad Zabidi ◽  
Norhafizah Ramli ◽  
Usman Ullah Sheikh
Keyword(s):  
Deep Learning ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Language Processing ◽  
National Language ◽  
Similar Work ◽  
Negative Sentiment ◽  
Work Done ◽  
Deep Learning Model ◽  
Bahasa Indonesia

<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>Twitter is an online microblogging and social-networking platform which allows users to write short messages called tweets. It has over 330 million registered users generating nearly 250 million tweets per day. As Malay is the national language in Malaysia, there is a significant number of users tweeting in Malay. Tweets have a maximum length of 140 characters which forces users to stay focused on the message they wish to disseminate. This characteristic makes tweets an interesting subject for sentiment analysis. Sentiment analysis is a natural language processing (NLP) task of classifying whether a tweet has a positive or negative sentiment. Tweets in Malay are chosen in this study as limited research has been done on this language. In this work, sentiment analysis applied to Malay tweets using the deep learning model. We achieved 77.59% accuracy which exceeds similar work done on Bahasa Indonesia.</p></td></tr></tbody></table>

scholarly journals Deep Learning Application to Ensemble Learning—The Simple, but Effective, Approach to Sentiment Classifying

2019 ◽  
Vol 9 (13) ◽  
pp. 2760 ◽  
Author(s):  
Khai Tran ◽  
Thi Phan
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Ensemble Learning ◽  
Language Processing ◽  
Short Term Memory ◽  
Learning Model ◽  
Sentiment Classification ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Deep Learning Model

Sentiment analysis is an active research area in natural language processing. The task aims at identifying, extracting, and classifying sentiments from user texts in post blogs, product reviews, or social networks. In this paper, the ensemble learning model of sentiment classification is presented, also known as CEM (classifier ensemble model). The model contains various data feature types, including language features, sentiment shifting, and statistical techniques. A deep learning model is adopted with word embedding representation to address explicit, implicit, and abstract sentiment factors in textual data. The experiments conducted based on different real datasets found that our sentiment classification system is better than traditional machine learning techniques, such as Support Vector Machines and other ensemble learning systems, as well as the deep learning model, Long Short-Term Memory network, which has shown state-of-the-art results for sentiment analysis in almost corpuses. Our model’s distinguishing point consists in its effective application to different languages and different domains.

scholarly journals LUX: Smart Mirror with Sentiment Analysis for Mental Comfort

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3092
Author(s):  
Hyona Yu ◽  
Jihyun Bae ◽  
Jiyeon Choi ◽  
Hyungseok Kim
Keyword(s):  
Mental Health ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Korean Text ◽  
News Reporting ◽  
Language Structure ◽  
Text Sentiment Analysis ◽  
Deep Learning Model

As COVID-19 solidifies its presence in everyday life, the interest in mental health is growing, resulting in the necessity of sentiment analysis. A smart mirror is suitable for encouraging mental comfort due to its approachability and scalability as an in-home AI device. From the aspect of natural language processing (NLP), sentiment analysis for Korean lacks an emotion dataset regarding everyday conversation. Its significant differences from English in terms of language structure make implementation challenging. The proposed smart mirror LUX provides Korean text sentiment analysis with the deep learning model, which examines GRU, LSTM, CNN, Bi-LSTM, and Bi-GRU networks. There are four emotional labels: anger, sadness, neutral, and happiness. For each emotion, there are three possible interactive responses: reciting wise sayings, playing music, and sympathizing. The implemented smart mirror also includes more-typical functions, such as a wake-up prompt, a weather reporting function, a calendar, a news reporting function, and a clock.

scholarly journals A COMBINED DEEP LEARNING MODEL FOR PERSIAN SENTIMENT ANALYSIS

2019 ◽  
Vol 20 (1) ◽  
pp. 129-139 ◽  
Author(s):  
Zahra Bokaee Nezhad ◽  
Mohammad Ali Deihimi
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Deep Learning Model

With increasing members in social media sites today, people tend to share their views about everything online. It is a convenient way to convey their messages to end users on a specific subject. Sentiment Analysis is a subfield of Natural Language Processing (NLP) that refers to the identification of users’ opinions toward specific topics. It is used in several fields such as marketing, customer services, etc. However, limited works have been done on Persian Sentiment Analysis. On the other hand, deep learning has recently become popular because of its successful role in several Natural Language Processing tasks. The objective of this paper is to propose a novel hybrid deep learning architecture for Persian Sentiment Analysis. According to the proposed model, local features are extracted by Convolutional Neural Networks (CNN) and long-term dependencies are learned by Long Short Term Memory (LSTM). Therefore, the model can harness both CNN's and LSTM's abilities. Furthermore, Word2vec is used for word representation as an unsupervised learning step. To the best of our knowledge, this is the first attempt where a hybrid deep learning model is used for Persian Sentiment Analysis. We evaluate the model on a Persian dataset that is introduced in this study. The experimental results show the effectiveness of the proposed model with an accuracy of 85%. ABSTRAK: Hari ini dengan ahli yang semakin meningkat di laman media sosial, orang cenderung untuk berkongsi pandangan mereka tentang segala-galanya dalam talian. Ini adalah cara mudah untuk menyampaikan mesej mereka kepada pengguna akhir mengenai subjek tertentu. Analisis Sentimen adalah subfield Pemprosesan Bahasa Semula Jadi yang merujuk kepada pengenalan pendapat pengguna ke arah topik tertentu. Ia digunakan dalam beberapa bidang seperti pemasaran, perkhidmatan pelanggan, dan sebagainya. Walau bagaimanapun, kerja-kerja terhad telah dilakukan ke atas Analisis Sentimen Parsi. Sebaliknya, pembelajaran mendalam baru menjadi popular kerana peranannya yang berjaya dalam beberapa tugas Pemprosesan Bahasa Asli (NLP). Objektif makalah ini adalah mencadangkan senibina pembelajaran hibrid yang baru dalam Analisis Sentimen Parsi. Menurut model yang dicadangkan, ciri-ciri tempatan ditangkap oleh Rangkaian Neural Convolutional (CNN) dan ketergantungan jangka panjang dipelajari oleh Long Short Term Memory (LSTM). Oleh itu, model boleh memanfaatkan kebolehan CNN dan LSTM. Selain itu, Word2vec digunakan untuk perwakilan perkataan sebagai langkah pembelajaran tanpa pengawasan. Untuk pengetahuan yang terbaik, ini adalah percubaan pertama di mana model pembelajaran mendalam hibrid digunakan untuk Analisis Sentimen Persia. Kami menilai model pada dataset Persia yang memperkenalkan dalam kajian ini. Keputusan eksperimen menunjukkan keberkesanan model yang dicadangkan dengan ketepatan 85%.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

scholarly journals Medical Text Classification Using Hybrid Deep Learning Models with Multihead Attention

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Sunil Kumar Prabhakar ◽  
Dong-Ok Won
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Text Classification ◽  
Patient Information ◽  
Classification Accuracy ◽  
Learning Model ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Medical Text ◽  
Deep Learning Model

To unlock information present in clinical description, automatic medical text classification is highly useful in the arena of natural language processing (NLP). For medical text classification tasks, machine learning techniques seem to be quite effective; however, it requires extensive effort from human side, so that the labeled training data can be created. For clinical and translational research, a huge quantity of detailed patient information, such as disease status, lab tests, medication history, side effects, and treatment outcomes, has been collected in an electronic format, and it serves as a valuable data source for further analysis. Therefore, a huge quantity of detailed patient information is present in the medical text, and it is quite a huge challenge to process it efficiently. In this work, a medical text classification paradigm, using two novel deep learning architectures, is proposed to mitigate the human efforts. The first approach is that a quad channel hybrid long short-term memory (QC-LSTM) deep learning model is implemented utilizing four channels, and the second approach is that a hybrid bidirectional gated recurrent unit (BiGRU) deep learning model with multihead attention is developed and implemented successfully. The proposed methodology is validated on two medical text datasets, and a comprehensive analysis is conducted. The best results in terms of classification accuracy of 96.72% is obtained with the proposed QC-LSTM deep learning model, and a classification accuracy of 95.76% is obtained with the proposed hybrid BiGRU deep learning model.

scholarly journals SENTIMENT ANALYSIS FOR SARCASTIC MESSAGES IN SOCIAL MEDIA USING DEEP LEARNING TECHNIQUES - AN EMPIRICAL STUDY

2021 ◽  
Vol 9 (2) ◽  
pp. 1051-1052
Author(s):  
K. Kavitha, Et. al.
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Empirical Study ◽  
Sentiment Analysis ◽  
Learning Strategies ◽  
Language Processing ◽  
The People ◽  
Learning Techniques ◽  
Key Terms ◽  
Learning Frameworks

Sentiments is the term of opinion or views about any topic expressed by the people through a source of communication. Nowadays social media is an effective platform for people to communicate and it generates huge amount of unstructured details every day. It is essential for any business organization in the current era to process and analyse the sentiments by using machine learning and Natural Language Processing (NLP) strategies. Even though in recent times the deep learning strategies are becoming more familiar due to higher capabilities of performance. This paper represents an empirical study of an application of deep learning techniques in Sentiment Analysis (SA) for sarcastic messages and their increasing scope in real time. Taxonomy of the sentiment analysis in recent times and their key terms are also been highlighted in the manuscript. The survey concludes the recent datasets considered, their key contributions and the performance of deep learning model applied with its primary purpose like sarcasm detection in order to describe the efficiency of deep learning frameworks in the domain of sentimental analysis.

A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis

2020 ◽  
Vol 12 (2) ◽  
pp. 21-34
Author(s):  
Mostefai Abdelkader
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Learning Approach ◽  
Probabilistic Representation ◽  
Effective Manner ◽  
Textual Data ◽  
Positive Class ◽  
Negative Class ◽  
Deep Learning Model

In recent years, increasing attention is being paid to sentiment analysis on microblogging platforms such as Twitter. Sentiment analysis refers to the task of detecting whether a textual item (e.g., a tweet) contains an opinion about a topic. This paper proposes a probabilistic deep learning approach for sentiments analysis. The deep learning model used is a convolutional neural network (CNN). The main contribution of this approach is a new probabilistic representation of the text to be fed as input to the CNN. This representation is a matrix that stores for each word composing the message the probability that it belongs to a positive class and the probability that it belongs to a negative class. The proposed approach is evaluated on four well-known datasets HCR, OMD, STS-gold, and a dataset provided by the SemEval-2017 Workshop. The results of the experiments show that the proposed approach competes with the state-of-the-art sentiment analyzers and has the potential to detect sentiments from textual data in an effective manner.

Deep Learning Techniques for Polarity Classification in Multimodal Sentiment Analysis

2018 ◽  
Vol 17 (03) ◽  
pp. 883-910 ◽  
Author(s):  
P. D. Mahendhiran ◽  
S. Kannimuthu
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Relevant Information ◽  
Neural Net ◽  
Video Information ◽  
Automatic Feature Extraction ◽  
Multimodal Sentiment Analysis

Contemporary research in Multimodal Sentiment Analysis (MSA) using deep learning is becoming popular in Natural Language Processing. Enormous amount of data are obtainable from social media such as Facebook, WhatsApp, YouTube, Twitter and microblogs every day. In order to deal with these large multimodal data, it is difficult to identify the relevant information from social media websites. Hence, there is a need to improve an intellectual MSA. Here, Deep Learning is used to improve the understanding and performance of MSA better. Deep Learning delivers automatic feature extraction and supports to achieve the best performance to enhance the combined model that integrates Linguistic, Acoustic and Video information extraction method. This paper focuses on the various techniques used for classifying the given portion of natural language text, audio and video according to the thoughts, feelings or opinions expressed in it, i.e., whether the general attitude is Neutral, Positive or Negative. From the results, it is perceived that Deep Learning classification algorithm gives better results compared to other machine learning classifiers such as KNN, Naive Bayes, Random Forest, Random Tree and Neural Net model. The proposed MSA in deep learning is to identify sentiment in web videos which conduct the poof-of-concept experiments that proved, in preliminary experiments using the ICT-YouTube dataset, our proposed multimodal system achieves an accuracy of 96.07%.

scholarly journals Comparative study of deep learning models for sentiment analysis

2018 ◽  
Vol 7 (2.14) ◽  
pp. 5726
Author(s):  
Oumaima Hourrane ◽  
El Habib Benlahmar ◽  
Ahmed Zellou
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Word Embeddings ◽  
Human Language ◽  
Learning Models ◽  
Automatic Learning ◽  
Learning Capability ◽  
The Web

Sentiment analysis is one of the new absorbing parts appeared in natural language processing with the emergence of community sites on the web. Taking advantage of the amount of information now available, research and industry have been seeking ways to automatically analyze the sentiments expressed in texts. The challenge for this task is the human language ambiguity, and also the lack of labeled data. In order to solve this issue, sentiment analysis and deep learning have been merged as deep learning models are effective due to their automatic learning capability. In this paper, we provide a comparative study on IMDB movie review dataset, we compare word embeddings and further deep learning models on sentiment analysis and give broad empirical outcomes for those keen on taking advantage of deep learning for sentiment analysis in real-world settings.

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