Emotion Classification of Online News Articles from the Reader's Perspective

Abstract. Multiple emotions are often evoked in readers in response to text stimuli like news article. In this paper, we present a novel method for classifying news sentences into multiple emotion categories using MultiLabel K Nearest Neighbor classification technique. The emotion data consists of 1305 news sentences and the emotion classes considered are disgust, fear, happiness and sadness. Words and polarity of subject, verb and object of the sentences and semantic frames have been used as features. Experiments have been performed on feature comparison and feature selection.

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“…The work [3] provides the method for ranking reader's emotions in Chinese news articles from Yahoo! Kimo News.…”

Section: Militant Attack Kills Over 30 Persons In Nigeriamentioning

confidence: 99%

Multi-label Text Classification Approach for Sentence Level News Emotion Analysis

Bhowmick

Basu

Mitra

et al. 2009

Lecture Notes in Computer Science

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“…For instance, we compare the negative sentiment ratio of each topic to compare the tourist complaints over topics; (2) In particular, to our knowledge, the topic-based sentiment analysis for the emotion classification method used in this study is the first to analyze emotions at the topic level. Previous emotion classification methods [30,31] were based on the document or sentence level and focused on the classification itself, without an in-depth analysis of these emotions. However, this study helps to classify sentiments into specific emotions (e.g., trust, joy, sadness, and disgust) over topics and facilitates an understanding of customer emotions after classifications.…”

Section: Discussionmentioning

confidence: 99%

“…Emotions can generally summarize a reviewer's feelings more generally than detailed sentiment words. Hence, many studies focus on emotion classification based on machine learning approaches [29][30][31][32] or rule-based approaches [33][34][35] and demonstrate the importance of emotion classification [32].…”

Section: Sentiment Analysismentioning

confidence: 99%

Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach

Ren

Hong

2017

Sustainability

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With the development of Web 2.0, many studies have tried to analyze tourist behavior utilizing user-generated contents. The primary purpose of this study is to propose a topic-based sentiment analysis approach, including a polarity classification and an emotion classification. We use the Latent Dirichlet Allocation model to extract topics from online travel review data and analyze the sentiments and emotions for each topic with our proposed approach. The top frequent words are extracted for each topic from online reviews on Ctrip.com. By comparing the relative importance of each topic, we conclude that many tourists prefer to provide "suggestion" reviews. In particular, we propose a new approach to classify the emotions of online reviews at the topic level utilizing an emotion lexicon, focusing on specific emotions to analyze customer complaints. The results reveal that attraction "management" obtains most complaints. These findings may provide useful insights for the development of attractions and the measurement of online destination image. Our proposed method can be used to analyze reviews from many online platforms and domains.

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“…트위터 외에도 감성 분석은 블로그 데이터 [108][109] , 뉴스 데이터 [110] , 인스타그램 [111] , 페이스북 [112] 그리고 영화 리뷰 [91,[113][114] [115] , 뉴스, 블로그, 트위터 데이터를 대 상으로 의미적 요소의 결합을 시도한 연구 [116] , 그리고 뉴스, 블로그, 트위터 데이터에 감성 분석을 통해 선 호 농식품 리스트를 추천한 연구 [117] 를 들 수 있다. …”

Section: 드로 해시태그를 활용한 준지도학습(Semi-supervisedunclassified

Investigations on Techniques and Applications of Text Analytics

Kim¹,

Lee²,

Choi³

et al. 2017

The Journal of Korean Institute of Communications and Informati

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The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs.Among the various types of unstructured data, text data have gained particular attention because it is the most representative method to describe and deliver information. Text analysis is generally performed in the following order: document collection, parsing and filtering, structuring, frequency analysis, and similarity analysis. The results of the analysis can be displayed through word cloud, word network, topic modeling, document classification, and semantic analysis. Notably, there is an increasing demand to identify trending topics from the rapidly increasing text data generated through various social media. Thus, research on and applications of topic modeling have been actively carried out in various fields since topic modeling is able to extract the core topics from a huge amount of unstructured text documents and provide the document groups for each different topic. In this paper, we review the major techniques and research trends of text analysis. Further, we also introduce some cases of applications that solve the problems in various fields by using topic modeling.

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Emotion Classification of Online News Articles from the Reader's Perspective

Cited by 82 publications

References 5 publications

Multi-label Text Classification Approach for Sentence Level News Emotion Analysis

Multi-label Text Classification Approach for Sentence Level News Emotion Analysis

Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach

Investigations on Techniques and Applications of Text Analytics

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