Bottom up: Exploring word emotions for Chinese sentence chief sentiment classification

Kang, Xin; Ren, Fuji; Wu, Yunong

doi:10.1109/nlpke.2010.5587793

Cited by 11 publications

(2 citation statements)

References 21 publications

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“…In fact, a recent study indicates that the complexity of the text emotions grows with the length of the text. In this study, we focus on sentence emotion analysis, as sentences are often considered to be the basic text pieces with self‐contained semantic meaning, and we think of it as a multilabel classification problem in which case every reasonable emotion label in the sentences would be identified.…”

Section: Introductionmentioning

confidence: 99%

Three predictions are better than one: Sentence multi‐emotion analysis from different perspectives

Kita

Matsumoto

2014

IEEJ Transactions Elec Engng

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Emotion prediction has been a core task in affective computing, which aims at finding the thorough human mental states by analyzing people's activities. In this paper, we focus on predicting emotions in the public online blogs from different people, by extracting as many reasonable emotions for each blog sentence as possible. Concretely, we consider three different perspectives for analyzing the multiple emotions in a sentence: (i) predict sentence emotions by examining the emotion related topics in a global sense; (ii) predict the sentence emotions from the context‐sensitive word emotions; and (iii) predict sentence emotions by considering the emotional significance in the local bag of words. We build different probabilistic models from each perspective, to separately generate the sentence emotion probabilities. We then integrate these probabilistic models to jointly predict the emotion probabilities. Because the component models are based on different emotional assumptions with distinct features, the integrated predictions should predict emotions from more general perspectives and therefore yield better results. In the experiment, we employ different evaluation criteria to compare the multi‐emotion predictions from the single and integrated models. Compared to the results in the baseline model, our bi‐integrated models achieve 8.69% higher Micro F1 and 7.78% higher Macro F1 scores, on average. Moreover, our tri‐integrated model acquires 10.00% higher Micro F1 and 9.19% higher Macro F1 scores than the baseline results, which proves our assumption, and suggests interesting features in the different emotion perspectives. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Introductionmentioning

confidence: 99%

Three predictions are better than one: Sentence multi‐emotion analysis from different perspectives

Kita

Matsumoto

2014

IEEJ Transactions Elec Engng

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“…However the emotions of words in lexicon are static while in realworld they could be flexible, so experiments based on emotion lexicon may suffer from insufficient or misleading emotion features. And the recent work [3] shows that more precise word emotions improve emotion analysis of sentences.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Importance of Modification Relation for Emotional Keywords Annotation and Emotion Types Recognition

Kita²,

Ren³

et al. 2011

IJIES

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Abstract:In this study, we make a scheme to explore the importance of modification relation for the emotional keywords annotation and emotion types recognition. We extract three modification features which are degree words, negative words and conjunctions from the Chinese emotion corpus named Ren-CECps. Beside word and part-ofspeech, three modification relations are adopted as feature in this study. We have carried out eight experiments with different feature sets for emotional keywords annotation and emotion types recognition in sentence level. Eight basic emotion types have been selected and Conditional Random Fields have been employed as the algorithm. In the part of evaluation, we demonstrate the importance of the modification features and our experiment results show the effectiveness of the modification features for improving the performance of emotional keywords annotation and emotion types recognition.

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Progressively Improving Supervised Emotion Classification Through Active Learning

Kang

Ren

2018

Lecture Notes in Computer Science

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Bottom up: Exploring word emotions for Chinese sentence chief sentiment classification

Cited by 11 publications

References 21 publications

Three predictions are better than one: Sentence multi‐emotion analysis from different perspectives

Three predictions are better than one: Sentence multi‐emotion analysis from different perspectives

Exploring the Importance of Modification Relation for Emotional Keywords Annotation and Emotion Types Recognition

Progressively Improving Supervised Emotion Classification Through Active Learning

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