Predicting political sentiments of voters from Twitter in multi-party contexts

Khatua, Aparup; Khatua, Apalak; Wang, Zhaoxia

doi:10.1016/j.asoc.2020.106743

Cited by 41 publications

(21 citation statements)

References 43 publications

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“…DL-based TSA DL-based techniques have a strong ability to automatically learn and discover discriminative feature representations from data themselves. DL-based TSA has been proved to be successful with the success of word embeddings [131] and the increase of the training data with multi-class classification [132]. Various DL-based approaches for TSA include deep convolutional neural network (ConvNet) learning, deep RNN learning, deep ConvNet-RNN learning and deep adversarial learning, as detailed next.…”

Section: 12mentioning

confidence: 99%

“…In recent years, more and more research teams have shifted their focus to applications of affective computing in real-life scenarios [417,418]. In order to detect emotions and sentiments from the textual information, the SenticNet directed by Erik Cambria of NTU applied the research outputs of affective computing [106,150,419] and sentiment analysis [22,[420][421][422][423][424] into many aspects of daily life, including HCI [425], finance [426] and social media monitoring and forecasting [132,427]. TSA is often used for recommender systems, by integrating diverse feedback information [428] or microblog texts [429].…”

Section: Applications Of Affective Computing In Real-life Scenariosmentioning

confidence: 99%

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A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances

Wang¹,

Song²,

Tao³

et al. 2022

Preprint

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Affective computing conjoins the research topics of emotion recognition and sentiment analysis, and can be realized with unimodal or multimodal data, consisting primarily of physical information (e.g., text, audio, and visual) and physiological signals (e.g., EEG and ECG). Physical-based affect recognition caters to more researchers due to the availability of multiple public databases, but it is challenging to reveal one's inner emotion hidden purposefully from facial expressions, audio tones, body gestures, etc. Physiological signals can generate more precise and reliable emotional results; yet, the difficulty in acquiring these signals hinders their practical application. Besides, by fusing physical information and physiological signals, useful features of emotional states can be obtained to enhance the performance of affective computing models. While existing reviews focus on one specific aspect of affective computing, we provide a systematical survey of important components: emotion models, databases, and recent advances. Firstly, we introduce two typical emotion models followed by five kinds of commonly used databases for affective computing. Next, we survey and taxonomize state-of-the-art unimodal affect recognition and multimodal affective analysis in terms of their detailed architectures and performances. Finally, we discuss some critical aspects of affective computing and its applications and conclude this review by pointing out some of the most promising future directions, such as the establishment of benchmark database and fusion strategies. The overarching goal of this systematic review is to help academic and industrial researchers understand the recent advances as well as new developments in this fast-paced, high-impact domain.

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Section: 12mentioning

confidence: 99%

Section: Applications Of Affective Computing In Real-life Scenariosmentioning

confidence: 99%

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances

Wang¹,

Song²,

Tao³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Disdainful fear [7] Fearful [8] Terrified [9] Anguished joy [10] Joy [11] Leisurely [12] Enjoyment [13] Free [14] Lucky [15] Love sadness [16] Sad [17] Upset [18] Depressed shame [19] Shame [20] Humiliated [21] Embarrassed guilt [22] Guilty [23] Regretful [24] Sinful module, that is, whether the propose self-attention module can still perform as expected if without semantic features or pre-trained word embedding. The above mentioned questions will be addressed one-by-one in the following.…”

Section: Labelsmentioning

confidence: 99%

“…Text emotion classification is an important branch of Natural Language Processing (NLP) research, aiming to identify the prominent emotion from short texts by predicting the label from a set of pre-defined emotions. Upon the identified emotions from user-generated texts (e.g., comments, reviews, blogs, and news reports), user attitudes and opinions can be retrieved and analyzed, and hence the task has great potential applications in various aspects of daily lives [17,22,43]. Existing works commonly extract semantic representation from texts via various deep modules in order to understand the semantic meaning for neural decision making.…”

Section: Introductionmentioning

confidence: 99%

EmoChannel-SA: exploring emotional dependency towards classification task with self-attention mechanism

Li¹,

Chen²,

Xie³

et al. 2021

World Wide Web

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Exploiting hand-crafted lexicon knowledge to enhance emotional or sentimental features at word-level has become a widely adopted method in emotion-relevant classification studies. However, few attempts have been made to explore the emotion construction in the classification task, which provides insights to how a sentence’s emotion is constructed. The major challenge of exploring emotion construction is that the current studies assume the dataset labels as relatively independent emotions, which overlooks the connections among different emotions. This work aims to understand the coarse-grained emotion construction and their dependency by incorporating fine-grained emotions from domain knowledge. Incorporating domain knowledge and dimensional sentiment lexicons, our previous work proposes a novel method named EmoChannel to capture the intensity variation of a particular emotion in time series. We utilize the resultant knowledge of 151 available fine-grained emotions to comprise the representation of sentence-level emotion construction. Furthermore, this work explicitly employs a self-attention module to extract the dependency relationship within all emotions and propose EmoChannel-SA Network to enhance emotion classification performance. We conducted experiments to demonstrate that the proposed method produces competitive performances against the state-of-the-art baselines on both multi-class datasets and sentiment analysis datasets.

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“…This approach is encouraged by the great success that has been achieved with machine learning techniques in many disciplines [11]- [13], including sentiment analysis, which is mostly supported today with the combination of machine learning and natural language processing [14]. Particular attention has been given to electoral research, such as prediction of the political sentiments of voters from short messages taking into account multiparty contexts in India [15], the detection of emerging political topics during German parliamentary elections comparing short messages with Google trends [16], or the evidence of negative sentiment in speeches from different actors from the US presidential election [17]. Some studies have obtained nontrivial information regarding election dynamics, such as the more persistent spreading of negative information than positive or neutral information about candidates, polarization in terms of sentiments spread by followers, and even the spread of misinformation by followers of the winner [18].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Political Tweets From the 2019 Spanish Elections

et al. 2021

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The use of sentiment analysis methods has increased in recent years across a wide range of disciplines. Despite the potential impact of the development of opinions during political elections, few studies have focused on the analysis of sentiment dynamics and their characterization from statistical and mathematical perspectives. In this paper, we apply a set of basic methods to analyze the statistical and temporal dynamics of sentiment analysis on political campaigns and assess their scope and limitations. To this end, we gathered thousands of Twitter messages mentioning political parties and their leaders posted several weeks before and after the 2019 Spanish presidential election. We then followed a twofold analysis strategy: (1) statistical characterization using indices derived from well-known temporal and information metrics and methods -including entropy, mutual information, and the Compounded Aggregated Positivity Index-allowing the estimation of changes in the density function of sentiment data; and (2) feature extraction from nonlinear intrinsic patterns in terms of manifold learning using autoencoders and stochastic embeddings. The results show that both the indices and the manifold features provide an informative characterization of the sentiment dynamics throughout the election period. We found measurable variations in sentiment behavior and polarity across the political parties and their leaders and observed different dynamics depending on the parties' positions on the political spectrum, their presence at the regional or national levels, and their nationalist or globalist aspirations.

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Predicting political sentiments of voters from Twitter in multi-party contexts

Cited by 41 publications

References 43 publications

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances

EmoChannel-SA: exploring emotional dependency towards classification task with self-attention mechanism

Sentiment Analysis of Political Tweets From the 2019 Spanish Elections

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