| 000 -LEADER |
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| fixed length control field |
a |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20220914092413.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220914b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
AIKTC-KRRC |
| Transcribing agency |
AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
17903 |
| Author |
Grover, Vandita |
| 245 ## - TITLE STATEMENT |
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| Title |
Exploiting emojis in sentiment analysis: a survey |
| 250 ## - EDITION STATEMENT |
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| Volume, Issue number |
Vol.103(1), Feb |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
New York |
| Name of publisher, distributor, etc. |
Springer |
| Year |
2022 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Pagination |
259-272p. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Sentiment analysis is now a prominent field of interest owing to a growing trend of users expressing their opinions on social media, review pages, feedback forms, and other online channels. The machine learning approach to sentiment analysis focuses on feature extraction methods like constructing lexicons to learn sentiment polarity or learning word embeddings and applying them for their use in machine learning algorithms for sentiment classification. But most popular machine learning approaches still cannot capture nuanced emotions like sarcasm, irony, etc. Emojis are now being used along with text by the users to express emotions and hence can help researchers improve sentiment classification tasks. Sentiment analysis powered by emojis is still in the nascent phase and has gained some pace in the last five years. The primary goal of this paper is to discuss the use of emojis that supplement the text to express different emotions. This paper compares some traditional text-based word embeddings and lexicons. Then the paper discusses the evolution of emoji-based lexicons and emoji embeddings. Further, some deep learning approaches using emojis to improve existing sentiment classification tasks are studied. The main contribution of this paper is to survey various approaches to use emojis in sentiment analysis which to the best of our knowledge has not been done till now. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| 9 (RLIN) |
4642 |
| Topical term or geographic name entry element |
Humanities and Applied Sciences |
| 773 0# - HOST ITEM ENTRY |
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| International Standard Serial Number |
2250-2106 |
| Title |
Journal of the institution of engineers (India): Series B |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| URL |
https://link.springer.com/article/10.1007/s40031-021-00620-7 |
| Link text |
Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
|
| Koha item type |
Articles Abstract Database |
