HEMOS: A novel deep learning-based fine-grained humor detecting method for sentiment analysis of social media

Li, Da; Rzepka, Rafał; Ptaszyński, Michał; Araki, Kenji

doi:10.1016/j.ipm.2020.102290

Cited by 63 publications

(35 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instead of classifying the data into Ternary and Binary Classification, the collected data can be categorized into Multi-class Classification [7,8,12,13,18,32,33,40,61,65,70] as a precise or accurate classification can be expected here. Mohammed Jabreel et al [32] state that "A multi-label problem is represented as one or more single-label (i.e., binary or multi class) problems.…”

Section: Multi-class Sentiment Analysismentioning

confidence: 99%

See 1 more Smart Citation

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

2021

View full text Add to dashboard Cite

Section: Multi-class Sentiment Analysismentioning

confidence: 99%

“…Word2Vec: Word2vec [11,14,38,44,53,56,65] a neural network model will be used to learn associations of words from a large corpus of text. A model can detect synonymous words or suggest additional words for a partial sentence, once trained.…”

Section: Negation Handlingmentioning

confidence: 99%

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

2021

View full text Add to dashboard Cite

“…Da Li et al [11] presented a fine-grained deep learning-based sentimental analysis model known as a Humor emoji slang-based system known as HEMOS for classifying the sentiments present in the Chinese language. The experiments are conducted by taking the frequently used 576 Chinese slangs as the slag lexicon and converting the 109 weibo emojis to textural features to obtain the Chinese emoji lexicons.…”

Section: Related Workmentioning

confidence: 99%

A Sentimental Classification Approach to Predict the User Behavior Using an Optimized Centered Convolutional Restricted Boltzmann Machine

Karthik

Sethukarasi²

2021

Preprint

View full text Add to dashboard Cite

Sentiment analysis uses different tools and techniques to extract informative data such as users' opinions or emotions from their textual feedback. The state-of-art sentiment analysis techniques offered lower performance due to the inability to handle both small and larger datasets. To overcome this problem this paper presents a deep learning technique known as Centered Convolutional Restricted Boltzmann Machines (CCRBM) for user behavioral sentimental analysis. However, this deep learning model's performance solely depends upon the parameter selection process. To overcome this problem and increase the classification accuracy a Hybrid Atom Search Arithmetic Optimization (HASAO) algorithm is used in this paper to select the parameters of the CCRBM architecture and offer optimal performance. The initial population quality and exploitation capacity of the Atom Search Optimization (ASO) algorithm is enhanced by hybridizing it with the Arithmetic Optimization(AO) algorithm. To investigate the effectiveness of the proposed HASAO optimized CCRBM architecture it is evaluated using four different datasets namely Reddit, Twitter, IMDB movie review, and Yelp dataset. The performance of the proposed model is analyzed by comparing it with four baseline models and the accuracy value above 90% for the four datasets proves the efficiency of the proposed technique.

show abstract

“…Chiruzzo et al (2019) proposed a regression task that predicts the humour score for a tweet. Li et al (2020) collected Chinese Internet slang expressions and combined them with a humor detecting method to analyse the sentiment of Weibo 4 posts. It should be noted that the examples in all of the corpora used or constructed in the above-mentioned studies are independent of each other.…”

Section: Related Workmentioning

confidence: 99%

Supporting Comedy Writers: Predicting Audience’s Response from Sketch Comedy and Crosstalk Scripts

Li¹

2020

Proceedings of the First Workshop on Computational Approaches to Discourse

View full text Add to dashboard Cite

Sketch comedy and crosstalk are two popular types of comedy. They can relieve people's stress and thus benefit their mental health, especially when performances and scripts are high-quality. However, writing a script is time-consuming and its quality is difficult to achieve. In order to minimise the time and effort needed for producing an excellent script, we explore ways of predicting the audience's response from the comedy scripts. For this task, we present a corpus of annotated scripts from popular television entertainment programmes in recent years. Annotations include a) text classification labels, indicating which actor's lines made the studio audience laugh; b) information extraction labels, i.e. the text spans that made the audience laughed immediately after the performers said them. The corpus will also be useful for dialogue systems and discourse analysis, since our annotations are based on entire scripts. In addition, we evaluate different baseline algorithms. Experimental results demonstrate that BERT models can achieve the best predictions among all the baseline methods. Furthermore, we conduct an error analysis and investigate predictions across scripts with different styles. 1 * The research was conducted during non-working time. The idea of this research was inspired by a discussion with my friend about an entertainment TV programme in which the comedians mentioned the difficulties of producing a highquality script.

show abstract

HEMOS: A novel deep learning-based fine-grained humor detecting method for sentiment analysis of social media

Cited by 63 publications

References 15 publications

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

A Sentimental Classification Approach to Predict the User Behavior Using an Optimized Centered Convolutional Restricted Boltzmann Machine

Supporting Comedy Writers: Predicting Audience’s Response from Sketch Comedy and Crosstalk Scripts

Contact Info

Product

Resources

About