An Insight on Sentiment Analysis Research from Text using Deep Learning Methods (original) (raw)

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 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 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.

A STUDY ON TWITTER SENTIMENT ANALYSIS USING DEEP LEARNING

IRJET, 2023

Sentiment analysis is a branch of research that examines feelings, attitudes, and reviews from many public spheres. Now-a-days, people share their thoughts and insights on a wide range of issues and topics via social media. Recently social networking sites like Twitter and Face book have become popular because users can able to express their opinions with other internet users through micro blogging. Today Twitter is among the most widely used blogging sites. But the disrespectful, insensitive, or unfair remarks that occasionally appear in online forums drive many people away. The majority of websites are unable to promote productive discourse, thus either heavily restrict or fully disable user comments. Insightful data about what is stated on Twitter is provided when sentiment analysis is combined with Twitter. This study analyzed with various deep-learning techniques for the classification of negative and positive elements. Data set SemEval-2017 from Twitter is used to train the final models and will be useful to identify the model which produces the most accurate results.

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.

IRJET- Sentiment analysis Using Machine Learning and Deep Learning: A Survey

IRJET, 2021

In recent years, internet strengthened itself as an influential platform that has changed the methods of business and communication. Sentiment Analysis (SA) become active research topic in the field of Natural Language Processing (NLP). It is the discipline that analyzes sentiment of people's opinions, attitude and emotions towards different entities such as products, services, organizations, individuals, issues, events, topics, and their attributes. The growth of web and social media such as blogs, business reviews, and social networks have fueled awareness in Sentiment Analysis (SA). There are several methods to analyze sentiments and all methods have numerous challenges, lacks and limitations. Therefore, this area still demands attention of researchers as well as industrialists. The main objective of this survey is to demonstrate an overview of Sentiment Analysis (SA) techniques and highlights limitations from previous studies. Our contribution will provide quick review of latest papers and help the researchers to choose appropriate for their future work. Finally, we attempt to compare the approaches and conclude that which approach can provide high accuracy and highlights more useful algorithms for the SA.

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.

Text mining based sentiment analysis using a novel deep learning approach

International Journal of Nonlinear Analysis and Applications, 2021

Leveraging text mining for sentiment analysis, and integrating text mining and deep learning are the main purposes of this paper. The presented study includes three main steps. At the first step, pre-processing such as tokenization, text cleaning, stop word, stemming, and text normalization has been utilized. Secondly, feature from review and tweets using Bag of Words (BOW) method and Term Frequency _Inverse Document Frequency is extracted. Finally, deep learning by dense neural networks is used for classification. This research throws light on understanding the basic concepts of sentiment analysis and then showcases a model which performs deep learning for classification for a movie review and airline$_$ sentiment data set. The performance measure in terms of precision, recall, F1-measure and accuracy were calculated. Based on the results, the proposed method achieved an accuracy of 95.3895.38%95.38 and 93.8493.84%93.84 for a movie review and Airline$_$ sentiment, respectively.

Twitter Messages Sentiment Analysis Model based on Deep and Machine Learning

European Journal of Scientific Research, 2017

This research aims at introducing a new hybrid model for Twitter sentiment analysis, which categorizes a tweet's sentiment polarity into positive and negative. Depending on the lexicon-based, regular machine learning-based and deep learning-based, these three methodologies were trialed, concluding that the out-turn polarity of each methodologies has become an input data for a majority voting algorithm. Through a simple rule, the voting algorithm reached the final polarity. Whereas the hybrid model promotes the accuracy of every approach individually on two data sets according to the experimental outcomes.

Sentiment Analysis using various Machine Learning and Deep Learning Techniques

Journal of the Nigerian Society of Physical Sciences

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution ...