A Comparison of Machine Learning and Deep Learning Methods with Rule Based Features for Mixed Emotion Analysis (original) (raw)
Related papers
Hybrid CNN Classification for Sentiment Analysis under Deep Learning
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...
An Optimized Deep Learning Model for Emotion Classification in Tweets
Computers, Materials & Continua
The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average man. Analyzing this data can be critical for any organization. Sentiments are often expressed with different intensity and topics which can provide great insight into how something affects society. Sentiment analysis in Twitter mitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in twitter. Resources used for analyzing tweet emotions are also briefly presented in literature survey section. In this paper, hybrid combination of different model's LSTM-CNN have been proposed where LSTM is Long Short Term Memory and CNN represents Convolutional Neural Network. Furthermore, the main contribution of our work is to compare various deep learning and machine learning models and categorization based on the techniques used. The main drawback of LSTM is that it's a timeconsuming process whereas CNN do not express content information in an accurate way, thus our proposed hybrid technique improves the precision rate and helps in achieving better results. Initial step of our mentioned technique is to preprocess the data in order to remove stop words and unnecessary data to improve the efficiency in terms of time and accuracy also it shows optimal results when it is compared with predefined approaches.
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.
Convolutional Neural Network Multi-Emotion Classifiers
Jordanian Journal of Computers and Information Technology, 2019
Natural languages are universal and flexible, but cannot exist without ambiguity. Having more than one attitude and meaning in the same phrase context is the main cause for word or phrase ambiguity. Most previous work on emotion analysis has only covered single-label classification and neglected the presence of multiple emotion labels in one instance. This paper presents multi-emotion classification in Twitter based on Convolutional Neural Networks (CNNs). The applied features are emotion lexicons, word embeddings and frequency distribution. The proposed networks performance is evaluated using state-of-the-art classification algorithms, achieving a hamming score range from 0.46 to 0.52 on the challenging SemEval2018 Task E-c.
IRJET- Emotion Detection from Tweets and Emoticons Using Machine Learning
IRJET, 2021
Many micro blogging sites have millions of people sharing their thoughts daily. We propose and investigate the sentiment from a popular real-time micro blogging service, Twitter, where real time reactions are posted by the user and we find their opinions for almost about "everything". Social networking sites like twitter, Facebook, Instagram, Orkut etc. are the great source of communication for internet users. So this becomes an important source for understanding the opinions, views or emotions of people. We extract data, i.e. tweets from Twitter in real time and apply machine learning techniques to convert them into a useful form and then use it for building sentiment classifier. Given a piece of written text, the problem is to categorize the text into one specific sentiment polarity i.e. positive, negative. With the increase use of Internet and big explosion of text data, it has been a very significant research subject to extract valuable information from Text Ocean. To realize multi-classification for text sentiment and emoticons sentiments, this paper promotes a RNN language model based on Long Short Term Memory (LSTM). LSTM is far better than the traditional RNN. And as a language model, LSTM is applied to achieve multiclassification for text and emoticon emotional attributes.
Emotion Detection from Text: Classification and Prediction of Moods in Real-Time Streaming Text
Emotion detection is a method which can be used to determine publics' attitudes, feelings, and feelings towards a specific target, such as persons, groups, organizations, various services, and products. Sentiment analysis is a superset of emotion detection since it infers the specific emotion rather than just declaring if something is good, bad, or neutral. Recent studies have concentrated on the use of verbal and facial clues to identify emotional states. Since written language lacks non-verbal indicators like voice tone, face expression, vocal pitch, etc., it can be challenging to discern emotions. The extraction of emotions from text has been proposed using a variety of natural language processing (NLP) techniques, such as the keyword-based approach, the lexicon-based approach, as well as deep learning approach. The disadvantages of keyword and lexicon-based approaches are numerous despite their focus on semantic links. In this research we suggest a BERT-based deep learning system (Fake BERT) by combining the BERT with numerous parallel blocks of a single-layer deep Convolutional Neural Network (CNN). GRU and Bi-GRU comparisons from past works were used as deep learning approaches. The proposed BERT-1D CNN generated the best results with an F1 score of 83.5, followed by Bi-GRU and GRU.
An Insight on Sentiment Analysis Research from Text using Deep Learning Methods
International journal of innovative technology and exploring engineering, 2019
Nowadays, Deep Learning (DL) is a fast growing and most attractive research field in the area of image processing and natural language processing (NLP), which is being adopted across several sectors like medicine, agriculture, commerce and so many other areas as well. This is mainly because of the greater advantages in using DL like automatic feature extraction, capability to process more number of parameters and capacity to generate more accuracy in results. In this paper, we have examined the research works which have used the DL based Sentiment Analysis (SA) for the social network data. This paper provides the brief explanation about the SA, the necessities of the pre-processing of text, performance metrics and the roles of DL models in SA. The main focus of this paper is to explore how the DL algorithms can enhance the performance of SA than the traditional machine learning algorithms for text based analysis. Since DL models are more effective for NLP research, the text classification can be applied on the complex sentences in which there are two inverse emotions which produces the two different emotions about an event. Through this literature appraisal we conclude that by using the Convolutional Neural Network (CNN) technique we can obtain more accuracy than others. The paper also brings to the light that there is no major focus on mixed emotions by using DL methods, which eventually increases the scope for future researches.
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.
Emotion Detection using CNN-LSTM based Deep Learning Model on Tweet Dataset
CERN European Organization for Nuclear Research - Zenodo, 2022
Emotion recognition from text is an important application of natural language processing. It has vast potential in many fields like marketing, artificial intelligence, political science, psychology etc. In recent times, more attention has been brought to this field because of availability and access to large amounts of opinionated data. Over the years many techniques have been proposed to tackle this problem. This paper focuses on the problem of emotion recognition from a dataset containing labelled tweets using a CNN-LSTM classifier model. The feature encoding for this model was done using the pre-trained Word2Vec word embedding and the model classified the tweets into five emotion classes: anger, sadness, joy, fear and love. The classifier was trained on 80% of the dataset and tested on the remaining 20%. The results of this proposed system was then compared with results from Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM) and Convolution Neural Network (CNN) models. The proposed system was found to outperform all of them with an accuracy of 93.3%.
IEEE Access
Emotion classification has become a valuable tool in analyzing text and emotions people express in response to events or crises, particularly on social media and other online platforms. The recent news about monkeypox highlighted various emotions individuals felt during the outbreak. People's opinions and concerns have been very different based on their awareness and understanding of the disease. Although there have been studies on monkeypox, emotion classification related to this virus has not been considered. As a result, this study aims to analyze the emotions individual expressed on social media posts related to the monkeypox disease. Our goal is to provide real-time information and identify critical concerns about the disease. To conduct our analysis, first, we extract and preprocess 800,000 datasets and then use NRCLexicon, a Python library, to predict and measure the emotional significance of each text. Secondly, we develop deep learning models based on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bi-directional LSTM (BiLSTM), and the combination of Convolutional Neural Networks and Long Short-Term Memory (CLSTM) for emotion classification. We use SMOTE (Synthetic Minority Oversampling Technique) and Random Undersampling techniques to address the class imbalance in our training dataset. The results of our study revealed that the CNN model achieved the highest performance with an accuracy of 96%. Overall, emotion classification on the monkeypox dataset can be a powerful tool for improving our understanding of the disease. The findings of this study will help develop effective interventions and improve public health. INDEX TERMS Monkeypox, emotion detection, deep learning, natural language processing (NLP), sentiment analysis.