The article examines and analyzes the linguistic and psychological features of political discourse using a computer-based Linguistic Inquiry and Word Count (LIWC) content analysis program to explore the relationship between political discourse and the personality of politicians. As for political discourse, it is perhaps the communicator, the linguistic personality, who plays the most important role in the communication. The linguistic personality of a politician is of particular interest in political discourse content-analysis, since it has the greatest influence on the public consciousness via mass media. Using text as a source of psychological and cognitive information has been gaining popularity. Researchers use a variety of methods to analyze texts, but Linguistic Inquiry Word Count (LIWC) has proved to be the most common technique. The analysis of linguistic patterns of political discourse shows that in the context of political speech events such as media interviews, politicians make a unique choice of lexical units, which can be interpreted as a manifestation of certain personality traits. However, despite the significance of the results, there are clear limitations to the use of computerized methodologies to make political discourse content-analysis, such as the limited interpretive capacity of software to understand pragmatic and contextual use of lexical units.

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Political discourse content analysis: a critical overview of a computerized text analysis program linguistic inquiry and word count (liwc)

The use of social media has grown exponentially over time with the growth of the Internet and has become the most influential networking platform in the 21st century. However, the enhancement of social connectivity often creates negative impacts on society that contribute to a couple of bad phenomena such as online abuse, harassment cyberbullying, cybercrime and online trolling. Cyberbullying frequently leads to serious mental and physical distress, particularly for women and children, and even sometimes force them to attempt suicide. Online harassment attracts attention due to its strong negative social impact. Many incidents have recently occurred worldwide due to online harassment, such as sharing private chats, rumours, and sexual remarks. Therefore, the identification of bullying text or message on social media has gained a growing amount of attention among researchers. The purpose of this research is to design and develop an effective technique to detect online abusive and bullying messages by merging natural language processing and machine learning. Two distinct freatures, namely Bag-of Words (BoW) and term frequency-inverse text frequency (TFIDF), are used to analyse the accuracy level of four distinct machine learning algorithms.

Cyberbullying Detection on Social Networks Using Machine Learning Approaches

Aggregated graph convolutional networks for aspect-based sentiment classification

Aspect-based sentiment analysis (ABSA) aims to analyze the sentiment polarity of an input sentence in a certain aspect. Many existing methods of ABSA employ long short-term memory (LSTM) networks and attention mechanism. However, the attention mechanism only models the local certain dependencies of the input information, which fails to capture the global dependence of the inputs. Simply improving the attention mechanism fails to solve the issue of target-sensitive sentiment expression, which has been proven to degrade the prediction effectiveness. In this work, we propose the multi-head self-attention transformation (MSAT) networks for ABSA tasks, which conducts more effective sentiment analysis with target specific self-attention and dynamic target representation. Given a set of review sentences, MSAT applies multi-head target specific self-attention to better capture the global dependence and introduces target-sensitive transformation to effectively tackle the problem of target-sensitive sentiment at first. Second, the part-of-speech (POS) features are integrated into MSAT to capture the grammatical features of sentences. A series of experiments carried on the SemEval 2014 and Twitter datasets show that the proposed model achieves better effectiveness compared with several state-of-the-art methods.

/pdf/multi-head-self-attention-transformation-networks-for-aspect-3tbfeu686c.pdf

Multi-Head Self-Attention Transformation Networks for Aspect-Based Sentiment Analysis

Graph convolutional network with multiple weight mechanisms for aspect-based sentiment analysis

Because social networks have become a vital part of people’s lives, cyberbullying becomes the most common risk encountered by young people on social networking platforms and raised serious concerns in society. Over the past few decades, most existing work on cyberbullying has focused on text analysis. Yet, the cyberbullying develops into multi-objective, multi-channel, and multi-form. Traditional text analysis methods cannot satisfy the diversity of bullying data in social networks. To deal with the new type of cyberbullying, we propose a multi-modal detection framework that takes into multi-modal information(e.g., image, video, comments, time) on social networks. Specifically, we not only extract textual features but also use the hierarchical attention networks to capture the session feature in social networks and encode several media information(e.g., video, image). Based on these features, we model the multi-modal cyberbullying detection framework to solve the new form of cyberbullying. Experimental analysis on two real-world datasets shows that our framework outperforms several existing state-of-the-art models.

Multi-modal cyberbullying detection on social networks

Residual attention and other aspects module for aspect-based sentiment analysis

Aspect-based sentiment analysis can predict the sentiment polarity of specific aspect terms in the text. Compared to general sentiment analysis, it extracts more useful information and analyzes the sentiment more accurately in the comment text. Many previous approaches use long short-term memory networks with attention mechanisms to directly learn aspect-specific representations and model comment text. However, these methods always ignore the importance of the aspect terms position and interactive information between the aspect terms and other words. To address these issues, we propose an improved model based on convolutional neural networks. First, a novel relative position encode layer can integrate the relative position information of specific aspect terms validly in a text. Second, by using the aspect attention mechanism, the semantic relationship between aspect terms and words in the text is fully considered. To verify the effectiveness of the proposed models, we conduct a large number of experiments and comparisons on seven public datasets. The experimental results show that this model outperforms to other state-of-the-art methods.

A relative position attention network for aspect-based sentiment analysis

The invention discloses an aspect-level sentiment analysis method based on a convolutional neural network. The method comprises the steps of establishing an aspect-level sentiment analysis model; constructing a relative position matrix of aspect-level information in the text through the model; fusing the text codes with the text codes; extracting a part of incidence matrixes corresponding to aspect-level information in the incidence matrixes; wherein the aspect-level emotion analysis model comprises a word embedding module, a relative position coding module, an aspect-level attention module and an sentiment classification module, and the aspect-level sentiment analysis model comprises a word embedding module, a relative position coding module, an aspect-level attention module and a sentiment classification module. According to the method, the sentiment of the aspect-level information in the text is modeled, so that the aspect-level emotion of the text is efficiently and accurately analyzed.

Aspect-level sentiment analysis method based on convolutional neural network

The invention discloses a high-power microwave heating early warning system and method based on infrared thermal imaging. The system comprises a microwave heating equipment end, an infrared thermal imaging module, a camera, a programmable logic console and an upper computer. The high-power microwave heating early warning system and method based on infrared thermal imaging is based on the high-power microwave heating process early warning system and method based on infrared thermal imaging, the temperature distribution and the highest temperature value of an industrial material in a high-powermicrowave heating environment can be acquired in real time, the temperature change of the heated material is intelligently adjusted according to the highest temperature value of the area, and safety accidents caused by overhigh temperature of a local area are effectively avoided.

Wang Kaige

Papers

Multi-modal cyberbullying detection on social networks

Residual attention and other aspects module for aspect-based sentiment analysis

A relative position attention network for aspect-based sentiment analysis

Aspect-level sentiment analysis method based on convolutional neural network

High-power microwave heating early warning system and method based on infrared thermal imaging