An improved method of extracting emotional attribute of relations based on syntactical analysis

Wang, Jun; Li, Lei; Ren, Fuji

doi:10.1109/ccis.2011.6045109

Cited by 1 publication

(2 citation statements)

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“…Actually, nearly all such pairs are assigned with the correct sentiment using the SD‐C method mentioned in Section . Refer to our previous work for more detailed explanation.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

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Recognizing sentiment of relations between entities in text

Wang

Ren

2014

IEEJ Transactions Elec Engng

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Recently, sentiment analysis for identifying positive or negative opinions from texts has received much attention. In this paper, we introduce sentiment analysis into a new field, which recognizes sentiment of relations between entities in the text. Three sentiment polarities between entities are recognized, namely positive, negative, and neutral. The difficulty in this work is that several pairs of entities may appear in the same sentence, and their sentiment polarities are determined by different related regions of the sentence. In addition, different features of words and their interactions in a related region will affect the final sentiment. It is difficult to process this using rigid rule‐based methods. Therefore, we propose a machine‐learning method based on statistics. In the proposed method, the model of conditional random fields (CRFs) is used to annotate the sentiment polarity between entities with the help of the syntactic dependency tree. The string of words that connects two entities in the dependency tree is used as the related region to recognize the sentiment. Experimental results and comparison with the other methods based on different principles and related regions suggest that the proposed method shows better performance and proves its validity. Moreover, the effect of different features on the CRF is word of the ith word in the sentence. We can get a CRF model using the CRF++ tool based on feature template and training corpus. After obtaining the model, sentiment of relations could be assigned automatically. The algorithm is shown in Fig. 2. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…Actually, nearly all such pairs are assigned with the correct sentiment using the SD‐C method mentioned in Section . Refer to our previous work for more detailed explanation.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

“…We had tried a rule‐based method for sentiment recognition, and the sentiment of the father word that attaches two countries decides sentiment of the country pair. It attained a precision of 79.66% in the two‐category solution . This simple method did not perform well especially when dealing with negative examples.…”

Section: Algorithm Descriptionmentioning

confidence: 90%