Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than Syntactic (original) (raw)
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Fine-grained emotion classification of Chinese microblogs based on graph convolution networks
World Wide Web, 2020
Microblogs are widely used to express people's opinions and feelings in daily life. Sentiment analysis (SA) can timely detect personal sentiment polarities through analyzing text. Deep learning approaches have been broadly used in SA but still have not fully exploited syntax information. In this paper, we propose a syntax-based graph convolution network (GCN) model to enhance the understanding of diverse grammatical structures of Chinese microblogs. In addition, a pooling method based on percentile is proposed to improve the accuracy of the model. In experiments, for Chinese microblogs emotion classification categories including happiness, sadness, like, anger, disgust, fear, and surprise, the F-measure of our model reaches 82.32% and exceeds the state-of-the-art algorithm by 5.90%. The experimental results show that our model can effectively utilize the information of dependency parsing to improve the performance of emotion detection. What is more, we annotate a new dataset for Chinese emotion classification, which is open to other researchers.
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.
Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets
arXiv (Cornell University), 2023
Emotion Classification based on text is a task with many applications which has received growing interest in recent years. This paper presents a preliminary study with the goal to help researchers and practitioners gain insight into relatively new datasets as well as emotion classification in general. We focus on three datasets that were recently presented in the related literature, and we explore the performance of traditional as well as state-of-the-art deep learning models in the presence of different characteristics in the data. We also explore the use of data augmentation in order to improve performance. Our experimental work shows that state-of-the-art models such as RoBERTa perform the best for all cases. We also provide observations and discussion that highlight the complexity of emotion classification in these datasets and test out the applicability of the models to actual social media posts we collected and labeled.
deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 2017
This working note presents the methodology used in deepCybErNet submission to the shared task on Emotion Intensities in Tweets (EmoInt) WASSA-2017. The goal of the task is to predict a real valued score in the range [0-1] for a particular tweet with an emotion type. To do this, we used Bag-of-Words and embedding based on recurrent network architecture. We have developed two systems and experiments are conducted on the Emotion Intensity shared Task 1 data base at WASSA-2017. A system which uses word embedding based on recurrent network architecture has achieved highest 5 fold crossvalidation accuracy. This has used embedding with recurrent network to extract optimal features at tweet level and logistic regression for prediction. These methods are highly language independent and experimental results shows that the proposed methods is apt for predicting a real valued score in than range [0-1] for a given tweet with its emotion type.
Proceedings of the 13th International Workshop on Semantic Evaluation
Emotion detection from user-generated contents is growing in importance in the area of natural language processing. The approach we proposed for the EmoContext task is based on the combination of a CNN and an LSTM using a concatenation of word embeddings. A stack of convolutional neural networks (CNN) is used for capturing the hierarchical hidden relations among embedding features. Meanwhile, a long short-term memory network (LSTM) is used for capturing information shared among words of the sentence. Each conversation has been formalized as a list of word embeddings, in particular during experimental runs pre-trained Glove and Google word embeddings have been evaluated. Surface lexical features have been also considered, but they have been demonstrated to be not usefully for the classification in this specific task. The final system configuration achieved a micro F1 score of 0.7089. The python code of the system is fully available at https://github. com/marcopoli/EmoContext2019.
EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning
Proceedings of the 13th International Workshop on Semantic Evaluation
Task 3, EmoContext, in the International Workshop SemEval 2019 provides training and testing datasets for the participant teams to detect emotion classes (Happy, Sad, Angry, or Others). This paper proposes a participating system (EmoDet) to detect emotions using deep learning architecture. The main input to the system is a combination of Word2Vec word embeddings and a set of semantic features (e.g. from AffectiveTweets Wekapackage). The proposed system (EmoDet) ensembles a fully connected neural network architecture and LSTM neural network to obtain performance results that show substantial improvements (F1-Score 0.67) over the baseline model provided by Task 3 organizers (F1score 0.58).
Predicting the Sentiment of Tweet Replies using Attentive Graph Convolutional Neural Networks
2024
Background and Objectives: Twitter is a microblogging platform for expressing assessments, opinions, and sentiments on different topics and events. While there have been several studies around sentiment analysis of tweets and their popularity in the form of the number of retweets, predicting the sentiment of first-order replies remained a neglected challenge. Predicting the sentiment of tweet replies is helpful for both users and enterprises. In this study, we define a novel problem; given just a tweet's text, the goal is to predict the overall sentiment polarity of its upcoming replies. Methods: To address this problem, we proposed a graph convolutional neural network model that exploits the text's dependencies. The proposed model contains two parallel branches. The first branch extracts the contextual representation of the input tweets. The second branch extracts the structural and semantic information from tweets. Specifically, a Bi-LSTM network and a self-attention layer are used in the first layer for extracting syntactical relations, and an affective knowledge-enhanced dependency tree is used in the second branch for extracting semantic relations. Moreover, a graph convolutional network is used on the top of these branches to learn the joint feature representation. Finally, a retrieval-based attention mechanism is used on the output of the graph convolutional network for learning essential features from the final affective picture of tweets. Results: In the experiments, we only used the original tweets of the RETWEET dataset for training the models and ignored the replies of the tweets in the training process. The results on three versions of the RETWEET dataset showed that the proposed model outperforms the LSTM-based models and similar state-of-the-art graph convolutional network models. Conclusion: The proposed model showed promising results in confirming that by using only the content of a tweet, we can predict the overall sentiment of its replies. Moreover, the results showed that the proposed model achieves similar or comparable results with simpler deep models when trained on a public tweet dataset such as ACL 2014 dataset while outperforming both simple deep models and state-of-the-art graph convolutional deep models when trained on the RETWEET dataset. This shows the proposed model's effectiveness in extracting structural and semantic relations in the tweets.
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.
In present days, people utilize social networking sites like Twitter for sharing their emotions and opinions. Detection and examination of the emotions present in the social media becomes advantageous in several application areas like e-commerce, government, public health, entertainment and so on. Several earlier studies have on sentiments and emotions classification has concentrated only on single label classification and does not considered the co-occurrence of many emotion classes in one tweet or post. This study presents a new multi-labeled emotion with intensity based sentiment classification in twitter data using convolutional neural networks called CNN-EISC model. The proposed CNN-EISC method has been validated using SEMEVAL2018 Task-1Emotion Intensity Ordinal Classification dataset. The CNN-EISC model has classified a set of four intensities namely Anger, Sad, Fear and Joy under diverse classes (0, 1, 2 and 3). The obtained experimental outcome stated that the CNN-EISC model has exhibited maximum results with the average precision of 77.08%, recall of 62.52%, F-measure of 65.76% and accuracy of 82.72%.
An investigation into the deep learning approach in sentimental analysis using graph-based theories
PLOS ONE, 2021
Sentiment analysis is a branch of natural language analytics that aims to correlate what is expressed which comes normally within unstructured format with what is believed and learnt. Several attempts have tried to address this gap (i.e., Naive Bayes, RNN, LSTM, word embedding, etc.), even though the deep learning models achieved high performance, their generative process remains a “black-box” and not fully disclosed due to the high dimensional feature and the non-deterministic weights assignment. Meanwhile, graphs are becoming more popular when modeling complex systems while being traceable and understood. Here, we reveal that a good trade-off transparency and efficiency could be achieved with a Deep Neural Network by exploring the Credit Assignment Paths theory. To this end, we propose a novel algorithm which alleviates the features’ extraction mechanism and attributes an importance level of selected neurons by applying a deterministic edge/node embeddings with attention scores on...