Deep Learning for Digital Text Analytics: Sentiment Analysis

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.

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.

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.

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...

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 ...

2019

Sentiment Analysis or Opinion Mining is a most popular field to analyze and find out insights from text data from various sources like News Articles, Twitter, etc. The medium of publishing news and events has become faster with the advancement of Information Technology (IT). IT has also been flooded with immense amounts of data, which is being published every minute of every day, by millions of users, in the shape of comments, blogs, news sharing through blogs, social media micro-blogging websites and many more. The medium of publishing news and events has become faster with the advancement of Information Technology (IT). IT has also been flooded with immense amounts of data, which is being published every minute of every day, by millions of users, in the shape of comments, blogs, news sharing through blogs, social media micro-blogging websites and many more. The medium of publishing news and events has become faster with the advancement of Information Technology (IT). IT has also b...

24TH TOPICAL CONFERENCE ON RADIO-FREQUENCY POWER IN PLASMAS, 2022

Abstract. Nowadays social media plays a significant role in all sorts of our activities ranging from analysing the attitude of a person for the job, getting opinions towards buying a product, acting as a forum for exchanging thoughts about the current events of various domains, creating awareness to the public about the natural calamities, educating the public about the fraudulent news spread by the fakers, initiating the young aspirant to protest against any societal issues, etc. Grasping the opinions shared by the experienced people towards a product, film, event, news, or politics like any subject of matter is one among the worth noting applications for a common man. It extends its application to making decisions about our day-to-day activities. The text reviews consist of enormous, sparse, non-uniform distribution of words represented as features. Text mining is the backend process for those applications. It includes techniques such as feature representation, sentiment classification, feature optimization, etc. Analysing the opinions suggested by the experienced people as positive and negative reviews is a challenging process and it is the baseline of our work. This paper contributes to the related processes involved in analysing the sentiments from the text reviews and accurately classifying them based on their polarity. In the proposed work, we particularly focus on feature representation techniques that have a major effect on enhancing the performance of sentiment classification. We explore different feature representation models such as TF-IDF vectorizer, word2vec vectorizer, and glove vectorizer as these word embedding models are interpreting the words and their syntactic and semantic relationships differently from the corpus. Also, we employ machine learning algorithms and a deep convolution neural network to perform comparative studies in classifying the sentiments. The word2vec in combination with Deep Convolution Neural Network provides the accuracy of 85.7%, precision of 84.4%, recall of 87%, and F- measure of 85.7% compared to other models.

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.

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.

Social media has given web users a venue for expressing and sharing their thoughts on different events. Facebook is one of a famous social media platform through which users can express their opinions on various events. Facebook is our targeted domain in this study. Sentiment analysis is a fundamental branch in natural language processing. It is the process of understanding sentiment in user-generated opinionated data in social media. Amharic language imposes many challenges, due to its complex structure, various dialects, in addition to the lack of its resources in the case of sentiment analysis task. The objective of this study is used to sentiment analysis model classify users' comments were written in Amharic as positive, negative or neutral. Deep learning has shown remarkable improvements in the field sentiment analysis. In this study, we have collected 13931 unstructured data from AMC official Facebook page using www.exportcomment.com and Facepage tool. It is difficult annotated each comment that were written in Amharic language. We have manually annotated by linguistic experts and also applied text preprocessing and representation techniques. After annotated, preprocessing and representing, the dataset prepared for the experiment purpose. The dataset, we used the 80/20 train-test splitting method, and train the model using tensorflow Keras deep learning library with LSTM and GUR classifiers as it confirmed to be successful at classification problems. Finally, in our experiment, we obtained the accuracy of LSTMis 95.5% and GRU is 96% respectively using word2vec embedding model.