Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings

Raksha Sharma, Arpan Somani, Lakshya Kumar, Pushpak Bhattacharyya


Abstract
Identification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a fine-grained sentiment analysis. For example, ‘master’, ‘seasoned’ and ‘familiar’ point to different intensity levels, though they all convey the same meaning (semantics), i.e., expertise: having a good knowledge of. In this paper, we propose a semi-supervised technique that uses sentiment bearing word embeddings to produce a continuous ranking among adjectives that share common semantics. Our system demonstrates a strong Spearman’s rank correlation of 0.83 with the gold standard ranking. We show that sentiment bearing word embeddings facilitate a more accurate intensity ranking system than other standard word embeddings (word2vec and GloVe). Word2vec is the state-of-the-art for intensity ordering task.
Anthology ID:
D17-1058
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
547–552
Language:
URL:
https://www.aclweb.org/anthology/D17-1058
DOI:
10.18653/v1/D17-1058
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1058.pdf