Sentimental Analysis and Deep Learning : A Survey (original) (raw)

A Survey of Deep Learning for Sentiment Analysis

2019

Deep learning has detonated in the public responsiveness, primarily as predictive and analytical products pervade our world, in the form of innumerable humancentered smart-world systems, including targeted advertisements, natural language assistants and interpreters, and mock-up self-driving vehicle systems. In contrast, researchers across disciplines have been including into their research to solve various natural language processing issues. In this paper we seek to provide a thorough exploration of Deep learning and its applications like sentimental analysis and natural language processing (NLP). Deep learning has an edge over the traditional machine learning algorithms, like support vector machine (SVM) and Naïve Bayes, for sentiment analysis because of its potential to overcome the challenges faced by sentiment analysis and handle the diversities involved, without the expensive demand for manual feature engineering. Deep learning models promise one thing given sufficient amount ...

Sentiment Analysis by using deep learning and Machine learning Techniques: A Review

International Journal of Advanced Trends in Computer Science and Engineering, 2021

More and more individuals are now using online social networks and resources throughout this day and age to not only interact and to communicate but also for sharing their views, experiences, ideas, impression about anything. The analysis of sentiments is the identification and categorization of these views to evaluate public opinions on a specific subject, question, product, etc. Day by day, the relevance of sentiment analysis is growing up. Machine learning is an area or field of computer science where, without being specifically programmed, computers can learn. Deep learning is the part of machine learning and deals with the algorithm, which is most widely used as Neural network, neural belief, etc., in which neuronal implementations are considered. For sentiment analysis, it compares their performance and accuracy so then it can be inferred that deep learning techniques in most of the cases provide better results. The gap in the precision of these two approaches, however, is not as important enough in certain situations, and so it is best to apply and use the machine learning approaches and methods because these are simpler in terms of Implementation.

Sentiment Analysis Using Deep Learning Techniques: A Review

International Journal of Advanced Computer Science and Applications

The World Wide Web such as social networks, forums, review sites and blogs generate enormous heaps of data in the form of users views, emotions, opinions and arguments about different social events, products, brands, and politics. Sentiments of users that are expressed on the web has great influence on the readers, product vendors and politicians. The unstructured form of data from the social media is needed to be analyzed and well-structured and for this purpose, sentiment analysis has recognized significant attention. Sentiment analysis is referred as text organization that is used to classify the expressed mind-set or feelings in different manners such as negative, positive, favorable, unfavorable, thumbs up, thumbs down, etc. The challenge for sentiment analysis is lack of sufficient labeled data in the field of Natural Language Processing (NLP). And to solve this issue, the sentiment analysis and deep learning techniques have been merged because deep learning models are effective due to their automatic learning capability. This Review Paper highlights latest studies regarding the implementation of deep learning models such as deep neural networks, convolutional neural networks and many more for solving different problems of sentiment analysis such as sentiment classification, cross lingual problems, textual and visual analysis and product review analysis, etc.

Sentiment Analysis Based on Deep Learning: A Comparative Study

Electronics, 2020

The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users’ opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input features.

Twitter Sentimental Analysis using Deep Learning Techniques

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020

There is a rapid growth in the domain of opinion mining as well as sentiment analysis which targets to discover the text or opinions present on the disparate social media plat- forms via machine-learning (ML) with polarity calculations, sentiment analysis or subjectivity analysis. Sentimental analysis (SA) indicates the text organization which is employed to cate- gorize the expressed feelings or mindset in diverse manners like favorable, thumbs up, positive, unfavorable, thumbs down, negative, etc. SA is a demanding and notable task that compris- es i) natural-language processing (NLP), ii) web mining and iii) ML. Also, to tackle this challenge, the SA is merged with deep learning (DL) techniques since DL models are efficient because of their automatic learning ability. This paper emphasizes re- cent studies regarding the execution of DL models like i) deep neural networks (DNN), ii) deep-beliefnetwork (DBN), iii) convolutional neural networks (CNN) together with, iv) re- current neural network (RNN) model. Those DL models aid in resolving different issues of SA like a) sentiment classification, b) the classification methods of i) rule-based classifiers(RBC), KNN and iii) SVM classification methods. Lastly, the classi- fication methods’ performance is contrasted in respect of accu- racy.

Deep Learning for Sentiment Analysis

2018

The opinions of people and others are one of the main influencers of human behaviour and activities. Therefore, individuals and organizations often consult with others to understand their opinions or attitudes towards a certain topic, before making decisions. Also, for telecommunication enterprises to survive, they need to be attentive to their customers’ opinions. Sentiment analysis is a technique that is often used by organizations to categorize and understand the underlying attitude of a person towards an entity, product, topic, etc. Though it has been traditionally performed using text-based sources, it has been suggested that other modalities should be explored. One such alternative to text-based sources is video recordings of people using or reviewing content. Videos can contain multiple modals including text, voice, and facial expressions, which can be used to detect a person’s attitude towards a topic. An approach to performing sentiment analysis using affective computing fo...

Traditional or deep learning for sentiment analysis : A review

Multidiszciplináris tudományok, 2022

Getting the context out of the text is the main objective of sentiment analysis. Today's digital world provides us with many data raw forms: Twitter, Facebook, blogs, etc. Researchers need to convert this raw data into useful information for performing analysis. Many researchers devoted their precious time to get the text's polarity using deep learning and conventional machine learning methods. In this paper, we reviewed both the approaches to gain insight into the work done. This paper will help the researchers to choose the best methods for classifying the text. We pick some of the best articles and critically analyze them in different parameters like dataset used, feature extraction technique, accuracy, and resource utilization.

SENTIMENT ANALYSIS BASED ON DEEP LEARNING

2018

Many emerging social sites, famous forums, review sites, and many bloggers generate huge amount of data in the form of user sentimental reviews, emotions, opinions, arguments, viewpoints etc. about different social events, products, brands, and politics, movies etc. Sentiments expressed by the users has great effect on readers, political images, online vendors. So the data present in scattered and unstructured manner needs to be managed properly and in this context sentiment analysis has got attention at very large level. Sentiment analysis can be defined as organization of the text which is used to understand the mindsets or feelings expressed in the form of different manners such as negative, positive, neutral, not satisfactory etc. This paper explains the implementation and accuracy of sentiment analysis using Tensor flow and python with any kind of text data. It works on embedding, LSTM and Sigmoid layers and finds the accuracy of data in iterative manner for better result.

Sentiment analysis with machine learning and deep learning: A survey of techniques and applications

International Journal of Science and Research Archive, 2024

Sentiment analysis is the task of automatically identifying the sentiment expressed in text. It has become increasingly important in many applications such as social media monitoring, product reviews analysis, and customer feedback evaluation. With the advent of deep learning techniques, sentiment analysis has seen significant improvements in performance and accuracy. This paper presents a comprehensive survey of machine learning and deep learning methods for sentiment analysis at the document, sentence, and aspect levels. We first provide an overview of traditional machine learning approaches to sentiment analysis and their limitations. We then look into various machine learning and deep learning architectures that have been successfully applied to this task. Additionally, we discuss the challenges of dealing with different data modalities, such as visual and multimodal data, and how both techniques have been adapted to address these challenges. Furthermore, we explore the applications of sentiment analysis in diverse domains, including social media, product reviews, and healthcare. Finally, we highlight the current limitations of deep learning approaches for sentiment analysis and outline potential future research directions. This survey aims to provide researchers and practitioners with a comprehensive understanding of the state-of-the-art deep learning techniques for sentiment analysis and their practical applications.

Different Techniques of Sentimental Analysis using Deep Learning

International Journal of Scientific and Research Publications (IJSRP)

World Wide Web like social media forums, review sites and blogs that generate a lot of data the type of users views, feelings, ideas and arguments about various social events, products, products, and politics. Emotions of users exposed to the web has a huge influence on it students, product retailers and politicians. Unstructured the type of data from social media is required for analysis and is well organized and for this purpose, emotional analysis has been required the saw important attention. Emotional analysis is called the textual structure used to distinguish the expressed attitude or emotions in different ways such as negative, positive, favoble, wrong, thumbs up, thumbs down, etc. This is a challenge emotion analysis lacks sufficient label data in the field Indigenous Languages (NLP) Processing. And to solve this problem, Emotional analysis and in-depthlearning strategies have been are integrated because in-depth learning models work for them the ability to read automatically. This Review Paper highlights the latest lessons on the implementation of in-depth learning models such as deep neural networks, convolutional neural networks as well many more to solve various emotional analysis problems such as emotional isolation, problems of different languages, text as well as visual analysis and product review analysis, etc.