A novel approach to the creation of a labelling lexicon for improving emotion analysis in text

Navarrete, Alejandra Andrea Segura; Martínez-Araneda, Claudia; Vidal‐Castro, Christian; Rubio-Manzano, Clemente

doi:10.1108/el-04-2020-0110

Cited by 8 publications

(6 citation statements)

References 23 publications

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“…Wei et al proposed a dynamic acoustic SL constructed from the acoustic lexical levels of different emotional categories to improve the ability of speech emotion recognition [17]. Navarrete et al redesigned and constructed a SL, focusing on raising the SA in the text, enriching the emotional intensity of words, and helping to solve the problems of cyberbullying and violence in the digital society [13]. Shoujian et al integrated existing emotion, degree, negative and network words to achieve effective SA on Weibo, and proposed a new method to construct SL, which improved the accuracy and recall rate of the method [14].…”

Section: Related Workmentioning

confidence: 99%

Design of Sentiment Analysis Framework of Digital Media Short Text Based on Multi-pattern Sentiment Lexicon

Lin

2023

SCPE

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Along the continuous advancement of the network and the rise of digital media, the amount of data produced by the exponential explosion. And how to use these data to provide personalized services for users is one of the current research focuses. To address the issue of insufficient coverage in the current sentiment lexicon and the difficulty of constructing sentiment lexicon in specific fields, this study proposes a multi-modal emotional thesaurus. Semi-supervised learning is used to solve the problem of insufficient coverage of emotional thesaurus, and a semi-supervised classification algorithm is realized by using a large number of unlabeled sample data combined with a small number of labeled sample data. Optimized learning is used to solve the problem of difficult construction of emotional thesaurus in specific fields, the corresponding specific emotional thesaurus is constructed by adaptive adjustment of emotional word score, and finally the improved emotional thesaurus is used to build a digital media short text sentiment analysis framework. For testing, the NLPCC dataset was used in this study, Experiments show that the framework constructed in this study requires 87 iterations, a Recall value of 0.912, a F1 value of 0.753, and an average accuracy of 83.39%, all of which are better than the sentiment analysis framework without the use of multi-pattern sentiment lexicon. In the simulation experiment, the recognition accuracy reached 85.88%, which was 16.85%, 11.57% and 6.72% higher than the test scenarios using a single emotion thesaurus selected in this study. The above results show that the digital media short-text sentiment analysis framework built in this research based on multi-pattern sentiment lexicon can carry out short-text sentiment analysis more accurately and efficiently, so as to accurately analyze users’ needs and provide customized services precisely.

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Section: Related Workmentioning

confidence: 99%

Design of Sentiment Analysis Framework of Digital Media Short Text Based on Multi-pattern Sentiment Lexicon

Lin

2023

SCPE

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“…Acheampong et al [15] F. A. Acheampong [20] surveyed the concept of emotion detection (ED) from texts and highlighted the main approaches adopted by researchers in the design of text-based ED systems. Navarrete Verma [16] P. Nandwani and R. Verma [21] described the process used to create an emotion lexicon enriched with the emotional intensity of words and focused on improving the emotion analysis process in texts [13]. Sailunaza and Alhajj [17] K. Sailunaz and R. Alhajj [22] used Twitter data to detect emotion and sentiment from text.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These models are a form of artificial intelligence (AI) that helps a machine learn and evolve without being explicitly programmed [ 16 ]. The preprocessed training dataset is then given as input into the CountVectorizer, TF-IDF Transformer, and MLClassifier to train the model and predict the emotions on the test dataset.…”

Section: Proposed Schemementioning

confidence: 99%

Text-Based Emotion Recognition Using Deep Learning Approach

Bharti

Varadhaganapathy²,

Gupta

et al. 2022

Computational Intelligence and Neuroscience

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Sentiment analysis is a method to identify people’s attitudes, sentiments, and emotions towards a given goal, such as people, activities, organizations, services, subjects, and products. Emotion detection is a subset of sentiment analysis as it predicts the unique emotion rather than just stating positive, negative, or neutral. In recent times, many researchers have already worked on speech and facial expressions for emotion recognition. However, emotion detection in text is a tedious task as cues are missing, unlike in speech, such as tonal stress, facial expression, pitch, etc. To identify emotions from text, several methods have been proposed in the past using natural language processing (NLP) techniques: the keyword approach, the lexicon-based approach, and the machine learning approach. However, there were some limitations with keyword- and lexicon-based approaches as they focus on semantic relations. In this article, we have proposed a hybrid (machine learning + deep learning) model to identify emotions in text. Convolutional neural network (CNN) and Bi-GRU were exploited as deep learning techniques. Support vector machine is used as a machine learning approach. The performance of the proposed approach is evaluated using a combination of three different types of datasets, namely, sentences, tweets, and dialogs, and it attains an accuracy of 80.11%.

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“…The second approach implemented used a mixture of the emotions analysis using Lexicons to form the features vector and ML classifiers. The Lexicon used is the one proposed in [33], which consists of an affective Lexicon in Spanish based on an enriched Lexicon, which represents the emotion intensity of each word, as shown in the example in Table 4. This Lexicon considered so-called emotional words.…”

Section: Lexicon Approachmentioning

confidence: 99%

Detecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language

et al. 2021

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In recent years, the use of social networks has increased exponentially, which has led to a significant increase in cyberbullying. Currently, in the field of Computer Science, research has been made on how to detect aggressiveness in texts, which is a prelude to detecting cyberbullying. In this field, the main work has been done for English language texts, mainly using Machine Learning (ML) approaches, Lexicon approaches to a lesser extent, and very few works using hybrid approaches. In these, Lexicons and Machine Learning algorithms are used, such as counting the number of bad words in a sentence using a Lexicon of bad words, which serves as an input feature for classification algorithms. This research aims at contributing towards detecting aggressiveness in Spanish language texts by creating different models that combine the Lexicons and ML approach. Twenty-two models that combine techniques and algorithms from both approaches are proposed, and for their application, certain hyperparameters are adjusted in the training datasets of the corpora, to obtain the best results in the test datasets. Three Spanish language corpora are used in the evaluation: Chilean, Mexican, and Chilean-Mexican corpora. The results indicate that hybrid models obtain the best results in the 3 corpora, over implemented models that do not use Lexicons. This shows that by mixing approaches, aggressiveness detection improves. Finally, a web application is developed that gives applicability to each model by classifying tweets, allowing evaluating the performance of models with external corpus and receiving feedback on the prediction of each one for future research. In addition, an API is available that can be integrated into technological tools for parental control, online plugins for writing analysis in social networks, and educational tools, among others.

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A novel approach to the creation of a labelling lexicon for improving emotion analysis in text

Cited by 8 publications

References 23 publications

Design of Sentiment Analysis Framework of Digital Media Short Text Based on Multi-pattern Sentiment Lexicon

Design of Sentiment Analysis Framework of Digital Media Short Text Based on Multi-pattern Sentiment Lexicon

Text-Based Emotion Recognition Using Deep Learning Approach

Detecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language

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