On the evaluation and combination of state-of-the-art features in Twitter sentiment analysis

Carvalho, Jonnathan; Plastino, Alexandre

doi:10.1007/s10462-020-09895-6

Cited by 74 publications

(43 citation statements)

References 93 publications

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“…Sanglerdsinlapachai et al summarize the current music sentiment analysis methods, classify them according to their recognition principles, and conduct a differentiated analysis of the advantages and disadvantages of different types of music sentiment analysis process [ 10 ]. According to different types of music data information, Carvalho and Plastino have performed ultra-high discrimination recognition of existing music sentiment analysis methods and realized high-accuracy analysis in terms of their composition and expression methods [ 11 ]. Based on the existing 5G technology, Huang et al perform multidimensional quantification of different types of music sentiment analysis processes and realize the construction of regular functions according to their differences [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Music Emotion Analysis Based on PSO‐BP Neural Network and Big Data Analysis

Chen

2021

Computational Intelligence and Neuroscience

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The current music teaching can effectively improve students’ music emotional expression indirectly. How to use the PSO-BP neural network to realize the quantitative research of music emotional expression is the current development trend. Based on this, this paper studies the influence factors of music emotion expression based on PSO-BP neural network and big data analysis. Firstly, a music emotion expression analysis model based on PSO-BP neural network algorithm is proposed. The autocorrelation function is used to simulate the emotion expression information in music. Through the maximum value of the autocorrelation function curve in the detection process, the vocal music signal is restored, and then the emotion expressed is analyzed. Secondly, the influence factors of PSO-BP neural network algorithm in music emotion expression are analyzed. The improved PSO-BP neural network algorithm and multidimensional data model are used for comprehensive analysis to accurately analyze the emotion in music expression, and the fuzzy evaluation method and analytic hierarchy process are used for quality evaluation. Finally, the validity of the music emotion analysis model is verified by many experiments.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Music Emotion Analysis Based on PSO‐BP Neural Network and Big Data Analysis

Chen

2021

Computational Intelligence and Neuroscience

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“…Similarly, Samant et al (2019) examined the predictive performance of supervised and unsupervised term weighting schemes for sentiment analysis on Twitter and they presented a novel improved supervised term weighting model. Recently, Carvalho and Plastino (2020)…”

Section: Onanmentioning

confidence: 99%

Ensemble of Classifiers and Term Weighting Schemes for Sentiment Analysis in Turkish

Onan

Üniversitesi²

2021

SRC

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With the advancement of information and communication technology, social networking and microblogging sites have become a vital source of information. Individuals can express their opinions, grievances, feelings, and attitudes about a variety of topics. Through microblogging platforms, they can express their opinions on current events and products. Sentiment analysis is a significant area of research in natural language processing because it aims to define the orientation of the sentiment contained in source materials. Twitter is one of the most popular microblogging sites on the internet, with millions of users daily publishing over one hundred million text messages (referred to as tweets). Choosing an appropriate term representation scheme for short text messages is critical. Term weighting schemes are critical representation schemes for text documents in the vector space model. We present a comprehensive analysis of Turkish sentiment analysis using nine supervised and unsupervised term weighting schemes in this paper. The predictive efficiency of term weighting schemes is investigated using four supervised learning algorithms (Naive Bayes, support vector machines, the k-nearest neighbor algorithm, and logistic regression) and three ensemble learning methods (AdaBoost, Bagging, and Random Subspace). The empirical evidence suggests that supervised term weighting models can outperform unsupervised term weighting models.

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“…In this equation,T i and T j represent the set of tri-grams of the tweets t i and t j , respectively. In case of tweets, unigrams, bigrams, and trigrams are the most adopted n-grams [42,43]. We have taken tri-grams with sliding window of size 1 instead of larger n-grams (4-grams or 5-grams) in our experiment.…”

Section: Self-referential Tweets Identificationmentioning

confidence: 99%

CAT-BiGRU: Convolution and Attention with Bi-Directional Gated Recurrent Unit for Self-Deprecating Sarcasm Detection

Kamal

Abulaish

2021

Cogn Comput

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Sarcasm detection has been a well-studied problem for the computational linguistic researchers. However, research related to different categories of sarcasm has still not gained much attention. Self-Deprecating Sarcasm (SDS) is a special category of sarcasm in which users apply sarcasm over themselves, and it is extensively used in social media platforms, mainly as an advertising tool for the brand endorsement, product campaign, and digital marketing with an aim to increase the sales volume. In this paper, we present a deep learning approach for detecting SDS on Twitter. We propose a novel Convolution and Attention with Bi-directional Gated Recurrent Unit (CAT-BiGRU) model, which consists of an input, embedding, convolutional, Bi-directional Gated Recurrent Unit (BiGRU), and two attention layers. The convolutional layer extracts SDS-based syntactic and semantic features from the embedding layer, BiGRU layer retrieves contextual information from the extracted features in both preceding and succeeding directions, and attention layers are used to retrieve SDS-based comprehensive context representation from the input texts. Finally, sigmoid function is employed to classify the input texts as a self-deprecating or non-self-deprecating sarcasm. Experiments are conducted over seven Twitter datasets to evaluate the proposed (CAT-BiGRU) model using standard evaluation metrics. The experimental results are impressive and significantly better than many neural network-based baselines and state-of-the-art methods. In this paper, we have highlighted biologically inspired and psychologically motivated basis of the proposed approach to examine its affective capabilities with respect to SenticNet. The efficacy of the proposed model is evaluated on two SenticNet-based sentic computing resources-Amazon word embedding and AffectiveSpace. Based on the experimental results, we conclude that deep learning-based approaches have potential to detect SDS in social media texts accurately.

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On the evaluation and combination of state-of-the-art features in Twitter sentiment analysis

Cited by 74 publications

References 93 publications

[Retracted] Music Emotion Analysis Based on PSO‐BP Neural Network and Big Data Analysis

[Retracted] Music Emotion Analysis Based on PSO‐BP Neural Network and Big Data Analysis

Ensemble of Classifiers and Term Weighting Schemes for Sentiment Analysis in Turkish

CAT-BiGRU: Convolution and Attention with Bi-Directional Gated Recurrent Unit for Self-Deprecating Sarcasm Detection

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