A Hybrid Deep Learning Architecture for Sentiment Analysis

International Journal of Advanced Trends in Computer Science and Engineering, 2020

In the Big Data world, there is an exponential increase in the volume of numerous data, such like text, image, audio and video, as text is the largest among them. Sentiment analysis is a trendy application in text mining, where text data concerning the feelings or attitude of the consumer is collected using different methods or techniques. Sentiment detection of online product reviews is helpful to figure out emotions and viewpoints of customers. Many researchers have just developed the Deep Learning model for obtaining tremendous performance in NLP. This paper suggested novel deep-learning hybrid architecture that is highly effective for analyzing sentiments on domain independent datasets. We blend deep Convolutional Neural Networks (CNN) and support vector machines (SVM) for a better overall classification. The reason for using CNN is not only to extract local features but also the framework for predicting sentiments and combining CNN output into SVM progress the classification. In addition, we have adopted MSPSO (Multi-swarm Particle Swarm Optimization) system to obtain optimized feature selection and to train the SVM for further improved in sentiment classification. To demonstrate that our proposed method is of a generic nature, we have evaluated on datasets of online product reviews from various domain. Evaluation exposes that the implementation of the proposed approach is reliable among all the datasets and often outshine the state-of-art systems.

2018

Sentiment analysis involves classifying text into positive, negative and neutral classes according to the emotions expressed in the text. Extensive study has been carried out in performing sentiment analysis using the traditional ‘bag of words’ approach which involves feature selection, where the input is given to classifiers such as Naive Bayes and SVMs. A relatively new approach to sentiment analysis involves using a deep learning model. In this approach, a recently discovered technique called word embedding is used, following which the input is fed into a deep neural network architecture. As sentiment analysis using deep learning is a relatively unexplored domain, we plan to perform in-depth analysis into this field and implement a state of the art model which will achieve optimal accuracy. The proposed methodology will use a hybrid architecture, which consists of CNNs (Convolutional Neural Networks) and RNNs (Recurrent Neural Networks), to implement the deep learning model on th...

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 ...

International Journal of Innovative Technology and Exploring Engineering, 2020

Sentiment Analysis (SA) is a popular field in Natural Language Processing (NLP) which focuses on the human emotions by analyzing the lexical and syntactic features. This paper presents an efficient method to find and extract the strong emotions for the sentiment classification using the proposed hybrid Convolutional Neural Networks - Global Vectors - Complex Sentence Searching - ABstract Noun Searching (CNN-GloVe-CSS-ABNS) model. The strong emotions are mostly found in the abstract nouns than the adjectives and adverbs present in the sentences. This research aims in extracting the complex sentences with abstract nouns for the sentiment classification from the twitter data. To extract the complex sentences, the proposed Complex Sentence Searching (CSS) algorithm was used. On the other hand, another proposed algorithm named, ABstract Noun Searching (ABNS) algorithm was used for identifying the abstract nouns in the sentences based on their position in the sentences. The results of thi...

The increasing volume of user-generated content on the web has made sentiment analysis an important tool for the extraction of information about the human emotional state. A current research focus for sentiment analysis is the improvement of granularity at aspect level, representing two distinct aims: aspect extraction and sentiment classification of product reviews and sentiment classification of target-dependent tweets. Deep learning approaches have emerged as a prospect for achieving these aims with their ability to capture both syntactic and semantic features of text without requirements for high-level feature engineering , as is the case in earlier methods. In this article, we aim to provide a comparative review of deep learning for aspect-based sentiment analysis to place different approaches in context.

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.

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.

ArXiv, 2021

Sentiment analysis is known as one of the most crucial tasks in the field of natural language processing and Convolutional Neural Network (CNN) is one of those prominent models that is commonly used for this aim. Although convolutional neural networks have obtained remarkable results in recent years, they are still confronted with some limitations. Firstly, they consider that all words in a sentence have equal contributions in the sentence meaning representation and are not able to extract informative words. Secondly, they require a large number of training data to obtain considerable results while they have many parameters that must be accurately adjusted. To this end, a convolutional neural network integrated with a hierarchical attention layer is proposed which is able to extract informative words and assign them higher weight. Moreover, the effect of transfer learning that transfers knowledge learned in the source domain to the target domain with the aim of improving the perform...

The International FLAIRS Conference Proceedings

Sentiment classification (SC) is an important natural language processing (NLP) task that aims to determine the sentiment or emotional tone in a given text. With the increasing pervasiveness of internet-based applications and social media, massive amounts of unstructured data are generated daily, elevating the opportunity and challenges associated with automated sentiment extraction for tasks such as customer feedback analysis, social media monitoring, and opinion mining. In this review paper, we provide an update on the state of the art in sentiment analysis, including an overview of and classification methods leveraging machine learning and deep learning methods.

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.