Using normal dictionaries to extract multiple semantic relationships

Li, Liang; Fang, Tian

doi:10.1049/joe.2019.1212

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…Human emotion has been a research hotspot of scholars since ancient times. In the field of modern psychology, the classification of emotions has developed from six primitive emotions proposed in "On Emotions" by Descartes to the multi-dimensional emotion model proposed by American psychologist Plutchik [2,8]. The model proposed by the latter is also known as the emotion wheel.…”

Section: Corpora Of Both Emotional Polarities and Tendenciesmentioning

confidence: 99%

“…A classification system, commonly known as the Chinese emotional classification studies, was proposed by Xu et al (2008) [10], which adds an emotional tendency of "like" to the six emotion classification systems of Ekman. sional emotion model proposed by American psychologist Plutchik [2,8]. The model proposed by the latter is also known as the emotion wheel.…”

Section: Corpora Of Both Emotional Polarities and Tendenciesmentioning

confidence: 99%

“…The weight ratio corresponding to each emotional tendency is calculated according to Formula (8), where m is the set of all weights. m max is the maximum value in the set m. m min is the minimum value in the set m.…”

Section: Judgment Of Emotional Tendencymentioning

confidence: 99%

“…There is an increasing need for emotion analysis of short texts for author recognition, customer review, Twitter personalisation, etc. [8]. Datasets of short texts, such as Broad Twitter Corpus Dataset, also offer a good base for further extracting emotional words.…”

Section: Introductionmentioning

confidence: 99%

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Developing Multi-Labelled Corpus of Twitter Short Texts: A Semi-Automatic Method

Liu,

Zhou,

Kong

et al. 2023

Systems

139

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Facing fast-increasing electronic documents in the Digital Media Age, the need to extract textual features of online texts for better communication is growing. Sentiment classification might be the key method to catch emotions of online communication, and developing corpora with annotation of emotions is the first step to achieving sentiment classification. However, the labour-intensive and costly manual annotation has resulted in the lack of corpora for emotional words. Furthermore, single-label semantic corpora could hardly meet the requirement of modern analysis of complicated user’s emotions, but tagging emotional words with multiple labels is even more difficult than usual. Improvement of the methods of automatic emotion tagging with multiple emotion labels to construct new semantic corpora is urgently needed. Taking Twitter short texts as the case, this study proposes a new semi-automatic method to annotate Internet short texts with multiple labels and form a multi-labelled corpus for further algorithm training. Each sentence is tagged with both the emotional tendency and polarity, and each tweet, which generally contains several sentences, is tagged with the first two major emotional tendencies. The semi-automatic multi-labelled annotation is achieved through the process of selecting the base corpus and emotional tags, data preprocessing, automatic annotation through word matching and weight calculation, and manual correction in case of multiple emotional tendencies are found. The experiments on the Sentiment140 published Twitter corpus demonstrate the effectiveness of the proposed approach and show consistency between the results of semi-automatic annotation and manual annotation. By applying this method, this study summarises the annotation specification and constructs a multi-labelled emotion corpus with 6500 tweets for further algorithm training.

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Section: Corpora Of Both Emotional Polarities and Tendenciesmentioning

confidence: 99%

Section: Corpora Of Both Emotional Polarities and Tendenciesmentioning

confidence: 99%

Section: Judgment Of Emotional Tendencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Developing Multi-Labelled Corpus of Twitter Short Texts: A Semi-Automatic Method

Liu,

Zhou,

Kong

et al. 2023

Systems

139

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“…Wei et al [22] pointed out another deep learning technique, Long Short-Term Memory (LSTM) which is used in Recursive Neural Network (RNN). LSTM which is capable of storing long-short memory information is adopted to store important information from documents and learning the same.…”

Section: Cmentioning

confidence: 99%

An Overview of Semantic Based Document Summarization in Different Languages

Ganesh¹,

Jaya²,

Sunitha³

2022

ECS Trans.

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The evolution of Artificial Intelligence (AI) gave insight into how humans should interact with machines. Natural Language Processing (NLP) is an area under AI, which aims to minimize the communication gap between human and machine. Text summarization is a sub-domain of NLP, which primarily focuses on summarizing text documents into a meaningful summary. Text summarization is useful in the areas, such as media monitoring, search engine optimization (SEO), question answering systems, etc. Lots of research works are carried out in the area of text summarization. A majority of the research works are adopted syntactic approaches. However, the semantic based approaches provide more meaningful summary compared to syntactic approaches. Due to the complexity of the semantic based methods, only few works are reported especially in Indian languages. In this paper, an overview of various semantic based text summarization methods are explored. An extensive review is done in languages, such as English, Arabic, Chinese, Hindi, Kannada, Tamil, and Malayalam. At the end of this survey, a critical analysis was done on various semantic based text summarization techniques. This analysis will not only give insights to potential prospective researchers, but also come up with an improved framework or method for semantic based abstractive text summarization.

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